cograph: Modern Network Visualization for R

Description

A modern, extensible network visualization package that provides high-quality static and interactive network plots. cograph accepts adjacency matrices, edge lists, or igraph objects and offers customizable layouts, node shapes, edge styles, and themes.

Main Functions

• cograph: Main entry point for creating network visualizations

• sn_layout: Apply layout algorithms

• sn_nodes: Customize node aesthetics

• sn_edges: Customize edge aesthetics

• sn_theme: Apply visual themes

• sn_render: Render to device

• sn_ggplot: Convert to ggplot2 object

Layouts

cograph provides several built-in layouts:

• circle: Nodes arranged in a circle

• spring: Fruchterman-Reingold force-directed layout

• groups: Group-based circular layout

• custom: User-provided coordinates

Themes

Built-in themes include:

• classic: Traditional network visualization style

• colorblind: Accessible color scheme

• gray: Grayscale theme

• dark: Dark background theme

• minimal: Clean, minimal style

Author(s)

Maintainer: Sonsoles López-Pernas sonsoles.lopez@uef.fi [copyright holder]

Authors:

• Mohammed Saqr [copyright holder]

• Santtu Tikka

See Also

Useful links:

• https://sonsoles.me/cograph/

• https://github.com/sonsoleslp/cograph

• Report bugs at https://github.com/sonsoleslp/cograph/issues

Add Cluster Borders

Description

Add Cluster Borders

Usage

.add_cluster_borders(p, cluster_list, node_order, border_color, border_width)

Add Cluster Labels

Description

Add Cluster Labels

Usage

.add_cluster_labels(p, cluster_list, node_order)

Add Value Labels to Heatmap (with optional halo)

Description

Add Value Labels to Heatmap (with optional halo)

Usage

.add_heatmap_values(
  p,
  value_size,
  value_color,
  value_fontface,
  value_fontfamily,
  value_halo,
  value_digits
)

Convert a cluster_summary to mcml (strip tna classes)

Description

Convert a cluster_summary to mcml (strip tna classes)

Usage

.as_mcml(cs)

Auto-detect clusters from cograph_network

Description

Auto-detect clusters from cograph_network

Usage

.auto_detect_clusters(x)

Build node-to-cluster lookup from cluster specification

Description

Build node-to-cluster lookup from cluster specification

Usage

.build_cluster_lookup(clusters, all_nodes)

Build cluster_summary from transition vectors

Description

Build cluster_summary from transition vectors

Usage

.build_from_transitions(
  from_nodes,
  to_nodes,
  weights,
  cluster_lookup,
  cluster_list,
  method,
  type,
  directed,
  compute_within,
  data = NULL
)

Build Base Heatmap

Description

Build Base Heatmap

Usage

.build_heatmap_base(
  df,
  colors,
  limits,
  midpoint,
  na_color,
  legend_title,
  show_legend,
  legend_position
)

Build MCML from edge list data.frame

Description

Build MCML from edge list data.frame

Usage

.build_mcml_edgelist(df, clusters, method, type, directed, compute_within)

Build MCML from sequence data.frame

Description

Build MCML from sequence data.frame

Usage

.build_mcml_sequence(df, clusters, method, type, directed, compute_within)

Build cell polygon data

Description

Build cell polygon data

Usage

.build_ml_cells(layers, skew, compress, layer_spacing)

Build inter-layer connection lines

Description

Build inter-layer connection lines

Usage

.build_ml_connections(layers, n_rows, n_cols, skew, compress, layer_spacing)

Build layer label positions

Description

Build layer label positions

Usage

.build_ml_labels(layers, n_rows, skew, compress, layer_spacing)

Build layer shell outlines

Description

Build layer shell outlines

Usage

.build_ml_shells(layers, n_rows, n_cols, skew, compress, layer_spacing)

Compute modularity

Description

Compute modularity

Usage

.compute_modularity(A, membership, directed = TRUE)

Create Unified cograph_network Object

Description

Internal constructor that creates a cograph_network object with the unified format. Both cograph() and as_cograph() use this to ensure identical output.

Usage

.create_cograph_network(
  nodes,
  edges,
  directed,
  meta = list(),
  weights = NULL,
  data = NULL,
  node_groups = NULL,
  type = NULL
)

Arguments

Value

A cograph_network object (named list with class).

Decode numeric tna_seq_data back to character labels

Description

Decode numeric tna_seq_data back to character labels

Usage

.decode_tna_data(data)

Detect input type for build_mcml

Description

Detect input type for build_mcml

Usage

.detect_mcml_input(x)

Disparity Filter Core Implementation

Description

Exact implementation matching TNA package. Uses fast .rowSums/.colSums for performance.

Usage

.disparity_filter_matrix(mat, level = 0.05)

Arguments

Value

Binary significance matrix.

Draw a Smooth Bezier Arc for Intra-Group Edges

Description

Uses a quadratic bezier curve with arc height proportional to distance, ensuring consistent rounded arcs for both close and distant node pairs (avoids the narrow spike issue with standard curvature for adjacent nodes).

Usage

.draw_intra_arc(
  x1,
  y1,
  x2,
  y2,
  intra_curvature,
  curve_sign,
  col,
  lwd,
  lty = 1,
  arrow,
  asize
)

Arguments

Draw Intra-Group Edges with Curvature

Description

Draws curved edges between nodes within the same group. Uses quadratic bezier arcs with height proportional to distance, producing consistent rounded arcs for both adjacent and distant node pairs.

Usage

.draw_intra_group_edges(
  layout_mat,
  weights,
  group_indices,
  edge_colors,
  intra_curvature,
  orientation,
  layout_type = "bipartite",
  threshold,
  directed
)

Arguments

Extract Initial Probabilities from Network Object

Description

Internal helper to extract initial probabilities (inits) from tna objects.

Usage

.extract_inits(x)

Arguments

Value

A numeric vector of initial probabilities, or NULL if not available.

Extract layers from various input types

Description

Extract layers from various input types

Usage

.extract_ml_layers(x, layer_list)

Extract Weight Matrix from Network Object

Description

Internal helper to extract adjacency/weight matrix from various input types.

Usage

.extract_weights(x)

Arguments

Value

A numeric matrix.

Convert a group_tna to mcml

Description

Two modes depending on whether clusters is provided:

With clusters (row-level)

For group_tna from tna::group_model(cluster_data(...)). clusters is the row-to-group assignments. Per-cluster tnas are taken as-is. Macro data is the assignments vector.

Without clusters (node-level)

For group_tna from as_tna(cluster_summary(...)). Cluster membership inferred from each tna's labels. Macro rebuilt from original data.

Usage

.group_tna_to_mcml(
  x,
  clusters = NULL,
  method = "sum",
  type = "tna",
  directed = TRUE,
  compute_within = TRUE
)

Heatmap Theme

Description

Heatmap Theme

Usage

.heatmap_theme(show_axis_labels, axis_text_size, axis_text_angle, aspect_ratio)

Convert Matrix to Long Format

Description

Convert Matrix to Long Format

Usage

.matrix_to_long(mat, row_labels, col_labels)

Normalize cluster specification to list format

Description

Normalize cluster specification to list format

Usage

.normalize_clusters(clusters, node_names)

Plot All Pairwise Comparisons

Description

Internal helper to plot all pairwise comparisons from a group_tna object.

Usage

.plot_compare_all_pairs(
  x,
  pos_color,
  neg_color,
  labels,
  show_inits,
  donut_inner_ratio,
  ...
)

Arguments

Value

Invisibly returns list of comparison results.

Plot Clustered Heatmap

Description

Plot Clustered Heatmap

Usage

.plot_heatmap_clustered(
  mat,
  cluster_list,
  cluster_spacing,
  show_legend,
  legend_position,
  legend_title,
  colors,
  limits,
  midpoint,
  na_color,
  show_values,
  value_size,
  value_color,
  value_fontface,
  value_fontfamily,
  value_halo,
  value_digits,
  show_diagonal,
  diagonal_color,
  cluster_labels,
  cluster_borders,
  border_color,
  border_width,
  show_axis_labels,
  axis_text_size,
  axis_text_angle,
  title,
  subtitle,
  xlab,
  ylab,
  aspect_ratio
)

Plot Group TNA as Supra-Adjacency Heatmap

Description

Plot Group TNA as Supra-Adjacency Heatmap

Usage

.plot_heatmap_group_tna(
  x,
  show_legend,
  legend_position,
  legend_title,
  colors,
  limits,
  midpoint,
  na_color,
  show_values,
  value_size,
  value_color,
  value_fontface,
  value_fontfamily,
  value_halo,
  value_digits,
  show_diagonal,
  diagonal_color,
  cluster_labels,
  cluster_borders,
  border_color,
  border_width,
  show_axis_labels,
  axis_text_size,
  axis_text_angle,
  title,
  subtitle,
  xlab,
  ylab,
  aspect_ratio
)

Plot Single Network Heatmap

Description

Plot Single Network Heatmap

Usage

.plot_heatmap_single(
  mat,
  show_legend,
  legend_position,
  legend_title,
  colors,
  limits,
  midpoint,
  na_color,
  show_values,
  value_size,
  value_color,
  value_fontface,
  value_fontfamily,
  value_halo,
  value_digits,
  show_diagonal,
  diagonal_color,
  row_labels,
  col_labels,
  show_axis_labels,
  axis_text_size,
  axis_text_angle,
  title,
  subtitle,
  xlab,
  ylab,
  aspect_ratio
)

Process weights based on type

Description

Process weights based on type

Usage

.process_weights(raw_weights, type, directed = TRUE)

Resolve Color Palette

Description

Resolve Color Palette

Usage

.resolve_colors(colors, values, limits, midpoint)

Resolve color palette

Description

Resolve color palette

Usage

.resolve_ml_colors(colors)

Resolve edge weights

Description

Resolve edge weights

Usage

.resolve_weights(g, weights)

TNA Visual Style Defaults

Description

Returns the standard TNA visual defaults as a named list. Used by splot(tna_styling = TRUE), from_tna(), and plot_tna().

Usage

.tna_style_defaults(n_nodes = NULL, directed = TRUE)

Arguments

Value

Named list of splot parameters.

Transform coordinates to perspective plane

Description

Transform coordinates to perspective plane

Usage

.transform_to_plane(x, y, layer_idx, n_layers, skew, compress, layer_spacing)

Translate qgraph-style parameter names to cograph equivalents

Description

When splot() receives a tna object, users often pass qgraph-style parameter names (e.g., size = 20, edge.color = "red") because the tna package uses qgraph for plotting. This function renames those keys to their cograph equivalents and applies value transforms where needed.

Usage

.translate_qgraph_dots(dots)

Arguments

Value

Named list with qgraph keys renamed to cograph equivalents.

Wrap igraph communities result

Description

Wrap igraph communities result

Usage

.wrap_communities(result, algorithm, g)

cograph Scaling Constants

Description

Central location for all scaling factors used in splot() and soplot(). These constants are calibrated to produce similar visual output to qgraph when using equivalent parameter values.

Usage

COGRAPH_SCALE

Format

A list with the following elements:

node_factor

Scale factor applied to node_size parameter

node_default

Default node size when not specified

label_default

Default label size (cex multiplier)

label_coupled

Whether label size is coupled to node size

edge_base

Base edge width

edge_scale

Edge width scale factor

edge_default

Default edge width

arrow_factor

Scale factor for arrow sizes

arrow_default

Default arrow size

Details

The default scaling mode uses values calibrated to match qgraph visual appearance:

• node_size = 6 in cograph should look similar to vsize = 6 in qgraph

• label_size = 1 uses cex-style multiplier (independent of node size)

• arrow_size = 1 produces consistent arrows between splot and soplot

Legacy mode preserves the original cograph v1.x behavior where:

• Node sizes used a 0.04 scale factor

• Label sizes were coupled to node size (vsize * 8)

• Arrow sizes differed between splot (0.03) and soplot (0.015)

Legacy Scaling Constants (Pre-v2.0 Behavior)

Description

Scaling constants that preserve the original cograph v1.x behavior. Use scaling = "legacy" to enable these values.

Usage

COGRAPH_SCALE_LEGACY

Format

A list with the same structure as COGRAPH_SCALE

CographLayout R6 Class

Description

Class for managing layout algorithms and computing node positions.

Value

A CographLayout R6 object.

Methods

Public methods

• CographLayout$new()

• CographLayout$compute()

• CographLayout$normalize_coords()

• CographLayout$get_type()

• CographLayout$get_params()

• CographLayout$print()

• CographLayout$clone()

Method new()

Create a new CographLayout object.

Usage

CographLayout$new(type = "circle", ...)

Arguments

Returns

A new CographLayout object.

Method compute()

Compute layout coordinates for a network.

Usage

CographLayout$compute(network, ...)

Arguments

Returns

Data frame with x, y coordinates.

Method normalize_coords()

Normalize coordinates to 0-1 range with padding.

Usage

CographLayout$normalize_coords(coords, padding = 0.1)

Arguments

Returns

Normalized coordinates.

Method get_type()

Get layout type.

Usage

CographLayout$get_type()

Returns

Character string.

Method get_params()

Get layout parameters.

Usage

CographLayout$get_params()

Returns

List of parameters.

Method print()

Print layout summary.

Usage

CographLayout$print()

Method clone()

The objects of this class are cloneable with this method.

Usage

CographLayout$clone(deep = FALSE)

Arguments

Examples

# Create a circular layout
layout <- CographLayout$new("circle")

# Apply to network
adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), nrow = 3)
net <- CographNetwork$new(adj)
coords <- layout$compute(net)

CographNetwork R6 Class

Description

Core class representing a network for visualization. Stores nodes, edges, layout coordinates, and aesthetic mappings.

Value

A CographNetwork R6 object.

Active bindings

Methods

Public methods

• CographNetwork$new()

• CographNetwork$clone_network()

• CographNetwork$set_nodes()

• CographNetwork$set_edges()

• CographNetwork$set_directed()

• CographNetwork$set_weights()

• CographNetwork$set_layout_coords()

• CographNetwork$set_node_aes()

• CographNetwork$set_edge_aes()

• CographNetwork$set_theme()

• CographNetwork$get_nodes()

• CographNetwork$get_edges()

• CographNetwork$get_layout()

• CographNetwork$get_node_aes()

• CographNetwork$get_edge_aes()

• CographNetwork$get_theme()

• CographNetwork$set_layout_info()

• CographNetwork$get_layout_info()

• CographNetwork$set_plot_params()

• CographNetwork$get_plot_params()

• CographNetwork$print()

• CographNetwork$clone()

Method new()

Create a new CographNetwork object.

Usage

CographNetwork$new(
  input = NULL,
  directed = NULL,
  nodes = NULL,
  simplify = FALSE
)

Arguments

Returns

A new CographNetwork object.

Method clone_network()

Clone the network with optional modifications.

Usage

CographNetwork$clone_network()

Returns

A new CographNetwork object.

Method set_nodes()

Set nodes data frame.

Usage

CographNetwork$set_nodes(nodes)

Arguments

Method set_edges()

Set edges data frame.

Usage

CographNetwork$set_edges(edges)

Arguments

Method set_directed()

Set directed flag.

Usage

CographNetwork$set_directed(directed)

Arguments

Method set_weights()

Set edge weights.

Usage

CographNetwork$set_weights(weights)

Arguments

Method set_layout_coords()

Set layout coordinates.

Usage

CographNetwork$set_layout_coords(coords)

Arguments

Method set_node_aes()

Set node aesthetics.

Usage

CographNetwork$set_node_aes(aes)

Arguments

Method set_edge_aes()

Set edge aesthetics.

Usage

CographNetwork$set_edge_aes(aes)

Arguments

Method set_theme()

Set theme.

Usage

CographNetwork$set_theme(theme)

Arguments

Method get_nodes()

Get nodes data frame.

Usage

CographNetwork$get_nodes()

Returns

Data frame with node information.

Method get_edges()

Get edges data frame.

Usage

CographNetwork$get_edges()

Returns

Data frame with edge information.

Method get_layout()

Get layout coordinates.

Usage

CographNetwork$get_layout()

Returns

Data frame with x, y coordinates.

Method get_node_aes()

Get node aesthetics.

Usage

CographNetwork$get_node_aes()

Returns

List of node aesthetic parameters.

Method get_edge_aes()

Get edge aesthetics.

Usage

CographNetwork$get_edge_aes()

Returns

List of edge aesthetic parameters.

Method get_theme()

Get theme.

Usage

CographNetwork$get_theme()

Returns

CographTheme object.

Method set_layout_info()

Set layout info.

Usage

CographNetwork$set_layout_info(info)

Arguments

Method get_layout_info()

Get layout info.

Usage

CographNetwork$get_layout_info()

Returns

List with layout information.

Method set_plot_params()

Set plot parameters.

Usage

CographNetwork$set_plot_params(params)

Arguments

Method get_plot_params()

Get plot parameters.

Usage

CographNetwork$get_plot_params()

Returns

List of plot parameters.

Method print()

Print network summary.

Usage

CographNetwork$print()

Method clone()

The objects of this class are cloneable with this method.

Usage

CographNetwork$clone(deep = FALSE)

Arguments

Examples

# Create network from adjacency matrix
adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), nrow = 3)
net <- CographNetwork$new(adj)

# Access properties
net$n_nodes
net$n_edges
net$is_directed

CographTheme R6 Class

Description

Class for managing visual themes for network plots.

Value

A CographTheme R6 object.

Active bindings

Methods

Public methods

• CographTheme$new()

• CographTheme$get()

• CographTheme$set()

• CographTheme$get_all()

• CographTheme$merge()

• CographTheme$clone_theme()

• CographTheme$print()

• CographTheme$clone()

Method new()

Create a new CographTheme object.

Usage

CographTheme$new(
  name = "custom",
  background = "white",
  node_fill = "#4A90D9",
  node_border = "#2C5AA0",
  node_border_width = 1,
  edge_color = "gray50",
  edge_positive_color = "#2E7D32",
  edge_negative_color = "#C62828",
  edge_width = 1,
  label_color = "black",
  label_size = 10,
  title_color = "black",
  title_size = 14,
  legend_background = "white"
)

Arguments

Returns

A new CographTheme object.

Method get()

Get a theme parameter.

Usage

CographTheme$get(name)

Arguments

Returns

Parameter value.

Method set()

Set a theme parameter.

Usage

CographTheme$set(name, value)

Arguments

Method get_all()

Get all theme parameters.

Usage

CographTheme$get_all()

Returns

List of parameters.

Method merge()

Merge with another theme.

Usage

CographTheme$merge(other)

Arguments

Returns

A new merged CographTheme.

Method clone_theme()

Clone the theme.

Usage

CographTheme$clone_theme()

Returns

A new CographTheme.

Method print()

Print theme summary.

Usage

CographTheme$print()

Method clone()

The objects of this class are cloneable with this method.

Usage

CographTheme$clone(deep = FALSE)

Arguments

Examples

# Create a custom theme
theme <- CographTheme$new(
  background = "white",
  node_fill = "steelblue",
  edge_color = "gray60"
)

qgraph Scaling Constants (Exact Values)

Description

Scaling constants that exactly replicate qgraph's visual formulas. Used by splot() for qgraph-compatible network visualization.

Usage

QGRAPH_SCALE

Format

A list with the following elements:

vsize_base

Base multiplier in vsize formula: 8

vsize_decay

Decay constant in vsize formula: 80

vsize_min

Minimum added to vsize: 1

vsize_factor

Scale factor to convert vsize to user coordinates: 0.015

esize_base

Base multiplier in esize formula: 15

esize_decay

Decay constant in esize formula: 90

esize_min

Minimum added to esize: 1

esize_unweighted

Default edge width for unweighted networks: 2

cent2edge_divisor

Divisor in cent2edge formula: 17.5

cent2edge_reference

Reference value in cent2edge: 2.16

cent2edge_plot_ref

Plot reference size: 7

curve_ref_diagonal

Diagonal reference for curve normalization: sqrt(98)

arrow_factor

Arrow size scale factor: 0.04

Abbreviate Labels

Description

Abbreviates labels to a maximum length, adding ellipsis if truncated.

Usage

abbrev_label(label, abbrev = NULL, n_labels = NULL)

label_abbrev(label, abbrev = NULL, n_labels = NULL)

Arguments

Value

Character vector of (possibly abbreviated) labels.

Examples

labels <- c("VeryLongStateName", "Short", "AnotherLongName")

# No abbreviation
abbrev_label(labels, NULL)

# Fixed max length
abbrev_label(labels, 5)  # "Very...", "Short", "Anot..."

# Auto-adaptive
abbrev_label(labels, "auto")

Edge Aesthetics

Description

Functions for setting edge aesthetic properties.

Node Aesthetics

Description

Functions for setting node aesthetic properties.

Aesthetic Scale Functions

Description

Functions for creating aesthetic scales.

Aggregate Duplicate Edges

Description

Combines duplicate edges by aggregating their weights using a specified function (sum, mean, max, min, or first).

Usage

aggregate_duplicate_edges(edges, method = "mean")

Arguments

Details

Aggregation Methods

sum

Total weight of all duplicate edges. Useful for frequency counts or when edges represent additive quantities (e.g., number of emails).

mean

Average weight. Useful for averaging multiple measurements or when duplicates represent repeated observations.

max

Maximum weight. Useful for finding the strongest connection or most recent value.

min

Minimum weight. Useful for the most conservative estimate or earliest value.

first

Keep first occurrence. Useful for preserving original order or when duplicates are erroneous.

The output edge list uses canonical node ordering (lower index first for undirected networks), ensuring consistent from/to assignment.

Value

A deduplicated data frame with the same columns as the input, where each node pair appears only once with its aggregated weight.

See Also

detect_duplicate_edges for identifying duplicates before aggregation

Aggregate Layers

Description

Combines multiple network layers into a single network.

Usage

aggregate_layers(
  layers,
  method = c("sum", "mean", "max", "min", "union", "intersection"),
  weights = NULL
)

lagg(
  layers,
  method = c("sum", "mean", "max", "min", "union", "intersection"),
  weights = NULL
)

Arguments

Value

Aggregated adjacency matrix

Examples

# layers <- list(L1 = mat1, L2 = mat2, L3 = mat3)
# aggregate_layers(layers, "sum")           # Total
# aggregate_layers(layers, "mean")          # Average
# aggregate_layers(layers, "union")         # Any edge
# aggregate_layers(layers, "intersection")  # All edges

Aggregate Edge Weights

Description

Aggregates a vector of edge weights using various methods. Compatible with igraph's edge.attr.comb parameter.

Usage

aggregate_weights(w, method = "sum", n_possible = NULL)

wagg(w, method = "sum", n_possible = NULL)

Arguments

Value

Single aggregated value

Examples

w <- c(0.5, 0.8, 0.3, 0.9)
aggregate_weights(w, "sum")   # 2.5
aggregate_weights(w, "mean")  # 0.625
aggregate_weights(w, "max")   # 0.9

Calculate Arrow Base Midpoint

Description

Returns the midpoint between the arrow wings (where the curve should end). This is used to connect the edge line to the back of the arrow head.

Usage

arrow_base_midpoint(x, y, angle, size, arrow_angle = pi/6)

Arguments

Value

List with x, y coordinates of the arrow base midpoint.

Calculate Arrow Head Points

Description

Returns the vertices for an arrow head polygon without drawing.

Usage

arrow_head_points(x, y, angle, size, arrow_angle = pi/6)

Arguments

Value

List with x, y vectors and midpoint coordinates.

Calculate Arrow Head Points

Description

Calculate Arrow Head Points

Usage

arrow_points(x, y, angle, size, width = 0.5, x_scale = 1, y_scale = 1)

Arguments

Value

List with arrow polygon coordinates and midpoint for line connection.

Calculate Arrow Radius

Description

Returns the distance from arrow tip to base midpoint. This is how far back from the tip the arrow extends.

Usage

arrow_radius(size, arrow_angle = pi/6)

Arguments

Value

The arrow radius (distance from tip to base).

Convert to Cograph Network

Description

Creates a lightweight cograph_network object from various network inputs. The resulting object is a named list with all data accessible via $.

Usage

as_cograph(x, directed = NULL, simplify = FALSE, ...)

to_cograph(x, directed = NULL, ...)

Arguments

Details

The cograph_network format is designed to be:

• Lean: Only essential data stored, computed values derived on demand

• Modern: Uses named list elements instead of attributes for clean $ access

• Compatible: Works seamlessly with splot() and other cograph functions

Use getter functions for programmatic access: get_nodes, get_edges, get_labels, n_nodes, n_edges

Use setter functions to modify: set_nodes, set_edges, set_layout

Value

A cograph_network object: a named list with components:

nodes

Data frame with id, label, (x, y if layout applied)

edges

Data frame with from, to, weight columns

directed

Logical indicating if network is directed

weights

Full n×n weight matrix (for to_matrix round-trip)

data

Original estimation data (sequence matrix, edge list, etc.), or NULL

meta

Consolidated metadata list with sub-fields: source (input type string), layout (layout info list or NULL), tna (TNA metadata or NULL)

node_groups

Optional node groupings data frame

A cograph_network object. See as_cograph.

See Also

get_nodes to extract the nodes data frame, get_edges to extract edges as a data frame, n_nodes and n_edges for counts, is_directed to check directedness, splot for plotting

Examples

# From adjacency matrix
mat <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), nrow = 3)
net <- as_cograph(mat)

# Direct $ access to core data
net$nodes      # nodes data frame
net$edges      # edges data frame
net$directed   # TRUE/FALSE

# Getter functions (recommended for programmatic use)
get_nodes(net)   # nodes data frame
get_edges(net)   # edges data frame (from, to, weight)
get_labels(net)  # character vector of labels
n_nodes(net)     # 3
n_edges(net)     # 3
cograph::is_directed(net) # FALSE (symmetric matrix)

# Setter functions
net <- set_nodes(net, data.frame(id = 1:3, label = c("A", "B", "C")))
net <- set_edges(net, data.frame(from = c(1,2), to = c(2,3), weight = c(0.5, 0.8)))
net <- set_layout(net, data.frame(x = c(0, 1, 0.5), y = c(0, 0, 1)))

# Plot it
splot(net)

# From igraph (if installed)
if (requireNamespace("igraph", quietly = TRUE)) {
  g <- igraph::make_ring(10)
  net <- as_cograph(g)
  splot(net)
}
mat <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), nrow = 3)
net <- to_cograph(mat)

Convert to mcml

Description

Convert various objects to the mcml class – a clean, tna-independent representation of a multilayer cluster network.

Usage

as_mcml(x, ...)

## S3 method for class 'cluster_summary'
as_mcml(x, ...)

## S3 method for class 'group_tna'
as_mcml(x, clusters = NULL, method = "sum", type = "tna", directed = TRUE, ...)

## S3 method for class 'mcml'
as_mcml(x, ...)

## Default S3 method:
as_mcml(x, ...)

Arguments

Value

An mcml object with components macro, clusters, cluster_members, and meta.

An mcml object.

An mcml object. When clusters is provided, macro$data contains the cluster assignments and macro$weights is NULL (the macro is the sequence of clusters, not a summary).

The input mcml object unchanged.

See Also

build_mcml, as_tna

Examples

# From cluster_summary
mat <- matrix(c(0.5, 0.2, 0.3,
                0.1, 0.6, 0.3,
                0.4, 0.1, 0.5), 3, 3, byrow = TRUE,
              dimnames = list(c("A", "B", "C"), c("A", "B", "C")))
clusters <- list(G1 = c("A", "B"), G2 = c("C"))
cs <- cluster_summary(mat, clusters, type = "tna")
m <- as_mcml(cs)
m$macro$weights

## Not run: 
# From group_tna with cluster assignments (requires tna + Nestimate)
seqs <- data.frame(T1 = c("A", "B", "A"), T2 = c("B", "A", "B"))
mod <- tna::tna(seqs)
cl <- Nestimate::cluster_data(mod, k = 2)
gt <- tna::group_model(cl)
m <- as_mcml(gt, clusters = cl$assignments)

## End(Not run)

Convert cluster_summary to tna Objects

Description

Converts a cluster_summary object to proper tna objects that can be used with all functions from the tna package. Creates a macro (cluster-level) tna model and per-cluster tna models (internal transitions within each cluster), returned as a flat group_tna object.

Usage

as_tna(x)

## S3 method for class 'cluster_summary'
as_tna(x)

## S3 method for class 'mcml'
as_tna(x)

## Default S3 method:
as_tna(x)

Arguments

Details

This is the final step in the MCML workflow, enabling full integration with the tna package for centrality analysis, bootstrap validation, permutation tests, and visualization.

Requirements

The tna package must be installed. If not available, the function throws an error with installation instructions.

Workflow

# Full MCML workflow
net <- cograph(edges, nodes = nodes)
net$nodes$clusters <- group_assignments
cs <- cluster_summary(net, type = "tna")
tna_models <- as_tna(cs)

# Now use tna package functions
plot(tna_models$macro)
tna::centralities(tna_models$macro)
tna::bootstrap(tna_models$macro, iter = 1000)

# Analyze per-cluster patterns
plot(tna_models$ClusterA)
tna::centralities(tna_models$ClusterA)

Excluded Clusters

A per-cluster tna cannot be created when:

• The cluster has only 1 node (no internal transitions possible)

• Some nodes in the cluster have no outgoing edges (row sums to 0)

These clusters are silently excluded. The macro (cluster-level) model still includes all clusters.

Value

A group_tna object (S3 class) – a flat named list of tna objects. The first element is named "macro" and represents the cluster-level transitions. Subsequent elements are named by cluster name and represent internal transitions within each cluster.

macro

A tna object representing cluster-level transitions. Contains $weights (k x k transition matrix), $inits (initial distribution), and $labels (cluster names). Use this for analyzing how learners/entities move between high-level groups or phases.

<cluster_name>

Per-cluster tna objects, one per cluster. Each tna object represents internal transitions within that cluster. Contains $weights (n_i x n_i matrix), $inits (initial distribution), and $labels (node labels). Clusters with single nodes or zero-row nodes are excluded (tna requires positive row sums).

A group_tna object (flat list of tna objects: macro + per-cluster).

A group_tna object (flat list of tna objects: macro + per-cluster).

A tna object constructed from the input.

See Also

cluster_summary to create the input object, plot_mcml for visualization without conversion, tna::tna for the underlying tna constructor

Examples

# -----------------------------------------------------
# Basic usage
# -----------------------------------------------------
mat <- matrix(runif(36), 6, 6)
diag(mat) <- 0
rownames(mat) <- colnames(mat) <- LETTERS[1:6]

clusters <- list(
  G1 = c("A", "B"),
  G2 = c("C", "D"),
  G3 = c("E", "F")
)

cs <- cluster_summary(mat, clusters, type = "tna")
tna_models <- as_tna(cs)

# Print summary
tna_models

# -----------------------------------------------------
# Access components
# -----------------------------------------------------
# Macro (cluster-level) tna
tna_models$macro
tna_models$macro$weights  # 3x3 transition matrix
tna_models$macro$inits    # Initial distribution
tna_models$macro$labels   # c("G1", "G2", "G3")

# Per-cluster tnas
names(tna_models)          # "macro", "G1", "G2", "G3"
tna_models$G1              # tna for cluster G1
tna_models$G1$weights      # 2x2 matrix (A, B)

# -----------------------------------------------------
# Use with tna package (requires tna)
# -----------------------------------------------------
if (requireNamespace("tna", quietly = TRUE)) {
  # Plot
  plot(tna_models$macro)
  plot(tna_models$G1)

  # Centrality analysis
  tna::centralities(tna_models$macro)
  tna::centralities(tna_models$G1)
  tna::centralities(tna_models$G2)
}

## Not run: 
# Bootstrap requires a tna object built from raw sequence data (has $data)
# as_tna() returns weight-matrix-based tnas which don't satisfy that requirement
if (requireNamespace("tna", quietly = TRUE)) {
  boot <- tna::bootstrap(tna_models$macro, iter = 1000)
  summary(boot)
}

## End(Not run)

# -----------------------------------------------------
# Check which within-cluster models were created
# -----------------------------------------------------
cs <- cluster_summary(mat, clusters, type = "tna")
tna_models <- as_tna(cs)

# All cluster names
names(cs$cluster_members)

# Clusters with valid per-cluster models
setdiff(names(tna_models), "macro")

# Clusters excluded (single node or zero rows)
setdiff(names(cs$cluster_members), names(tna_models))

Aspect-Corrected atan2

Description

Calculate angle accounting for aspect ratio differences.

Usage

atan2_usr(dy, dx)

Arguments

Value

Angle in radians.

Calculate Bezier Curve Points

Description

Calculate points along a quadratic Bezier curve.

Usage

bezier_points(x0, y0, x1, y1, x2, y2, n = 50)

Arguments

Value

Data frame with x, y coordinates.

Generate Brain Vertices

Description

Generate Brain Vertices

Usage

brain_vertices(x, y, r, n = 80)

Arguments

Value

List with x, y vectors of vertices.

Build Edge Labels from Template

Description

Generates edge labels for all edges using template formatting.

Usage

build_edge_labels_from_template(
  template = NULL,
  style = "none",
  weights = NULL,
  ci_lower = NULL,
  ci_upper = NULL,
  p_values = NULL,
  stars = NULL,
  digits = 2,
  p_digits = 3,
  p_prefix = "p=",
  ci_format = "bracket",
  oneline = TRUE,
  leading_zero = TRUE,
  n
)

Arguments

Value

Character vector of formatted labels.

Build MCML from Raw Transition Data

Description

Builds a Multi-Cluster Multi-Level (MCML) model from raw transition data (edge lists or sequences) by recoding node labels to cluster labels and counting actual transitions. Unlike cluster_summary which aggregates a pre-computed weight matrix, this function works from the original transition data to produce the TRUE Markov chain over cluster states.

Usage

build_mcml(
  x,
  clusters = NULL,
  method = c("sum", "mean", "median", "max", "min", "density", "geomean"),
  type = c("tna", "frequency", "cooccurrence", "semi_markov", "raw"),
  directed = TRUE,
  compute_within = TRUE
)

Arguments

Value

A cluster_summary object with meta$source = "transitions", fully compatible with plot_mcml, as_tna, and splot.

See Also

cluster_summary for matrix-based aggregation, as_tna to convert to tna objects, plot_mcml for visualization

Examples

# Edge list with clusters
edges <- data.frame(
  from = c("A", "A", "B", "C", "C", "D"),
  to   = c("B", "C", "A", "D", "D", "A"),
  weight = c(1, 2, 1, 3, 1, 2)
)
clusters <- list(G1 = c("A", "B"), G2 = c("C", "D"))
cs <- build_mcml(edges, clusters)
cs$macro$weights

# Sequence data with clusters
seqs <- data.frame(
  T1 = c("A", "C", "B"),
  T2 = c("B", "D", "A"),
  T3 = c("C", "C", "D"),
  T4 = c("D", "A", "C")
)
cs <- build_mcml(seqs, clusters, type = "raw")
cs$macro$weights

Calculate Point on Node Boundary

Description

Given a node center, size, and angle, calculates the point on the node boundary. Works with various shapes.

Usage

cent_to_edge(x, y, angle, cex, cex2 = NULL, shape = "circle")

Arguments

Value

List with x, y coordinates on boundary.

Calculate Network Centrality Measures

Description

Computes centrality measures for nodes in a network and returns a tidy data frame. Accepts matrices, igraph objects, cograph_network, or tna objects.

Usage

centrality(
  x,
  measures = "all",
  mode = "all",
  normalized = FALSE,
  weighted = TRUE,
  directed = NULL,
  loops = TRUE,
  simplify = "sum",
  digits = NULL,
  sort_by = NULL,
  cutoff = -1,
  invert_weights = NULL,
  alpha = 1,
  damping = 0.85,
  personalized = NULL,
  transitivity_type = "local",
  isolates = "nan",
  lambda = 1,
  k = 3,
  states = NULL,
  ...
)

Arguments

Details

The following centrality measures are available:

degree

Count of edges (supports mode: in/out/all)

strength

Weighted degree (supports mode: in/out/all)

betweenness

Shortest path centrality

closeness

Inverse distance centrality (supports mode: in/out/all)

eigenvector

Influence-based centrality

pagerank

Random walk centrality (supports damping and personalization)

authority

HITS authority score

hub

HITS hub score

eccentricity

Maximum distance to other nodes (supports mode)

coreness

K-core membership (supports mode: in/out/all)

constraint

Burt's constraint (structural holes)

transitivity

Local clustering coefficient (supports multiple types)

harmonic

Harmonic centrality - handles disconnected graphs better than closeness (supports mode: in/out/all)

diffusion

Diffusion degree centrality - sum of scaled degrees of node and its neighbors (supports mode: in/out/all, lambda scaling)

leverage

Leverage centrality - measures influence over neighbors based on relative degree differences (supports mode: in/out/all)

kreach

Geodesic k-path centrality - count of nodes reachable within distance k (supports mode: in/out/all, k parameter)

alpha

Alpha/Katz centrality - influence via paths, penalized by distance. Similar to eigenvector but includes exogenous contribution

power

Bonacich power centrality - measures influence based on connections to other influential nodes

subgraph

Subgraph centrality - participation in closed loops/walks, weighting shorter loops more heavily

laplacian

Laplacian centrality using Qi et al. (2012) local formula. Matches NetworkX and centiserve::laplacian()

load

Load centrality - fraction of all shortest paths through node, similar to betweenness but weights paths by 1/count

current_flow_closeness

Information centrality - closeness based on electrical current flow (requires connected graph)

current_flow_betweenness

Random walk betweenness - betweenness based on current flow rather than shortest paths (requires connected graph)

voterank

VoteRank - identifies influential spreaders via iterative voting mechanism. Returns normalized rank (1 = most influential)

percolation

Percolation centrality - importance for spreading processes. Uses node states (0-1) to weight paths. When all states equal, equivalent to betweenness. Useful for epidemic/information spreading analysis.

Value

A data frame with columns:

• node: Node labels/names

• One column per measure, with mode suffix for directional measures (e.g., degree_in, closeness_all)

Examples

# Basic usage with matrix
adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality(adj)

# Specific measures
centrality(adj, measures = c("degree", "betweenness"))

# Directed network with normalization
centrality(adj, mode = "in", normalized = TRUE)

# Sort by pagerank
centrality(adj, sort_by = "pagerank", digits = 3)

# PageRank with custom damping
centrality(adj, measures = "pagerank", damping = 0.9)

# Harmonic centrality (better for disconnected graphs)
centrality(adj, measures = "harmonic")

# Global transitivity
centrality(adj, measures = "transitivity", transitivity_type = "global")

Alpha (Katz) Centrality

Description

Influence via all paths penalized by distance. Similar to eigenvector centrality but includes an exogenous contribution, making it well-defined even for directed acyclic graphs.

Usage

centrality_alpha(x, mode = "all", ...)

Arguments

Value

Named numeric vector of alpha centrality values.

See Also

centrality for computing multiple measures at once, centrality_eigenvector for a related measure.

Examples

adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality_alpha(adj)

HITS Authority and Hub Scores

Description

Kleinberg's HITS algorithm. centrality_authority scores nodes pointed to by good hubs. centrality_hub scores nodes that point to good authorities.

Usage

centrality_authority(x, ...)

centrality_hub(x, ...)

Arguments

Value

Named numeric vector of authority or hub scores.

See Also

centrality for computing multiple measures at once.

Examples

adj <- matrix(c(0, 1, 0, 0, 0, 1, 1, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality_authority(adj)
centrality_hub(adj)

Betweenness Centrality

Description

Fraction of shortest paths passing through each node. Nodes with high betweenness act as bridges connecting different parts of the network.

Usage

centrality_betweenness(x, ...)

Arguments

Value

Named numeric vector of betweenness values.

See Also

centrality for computing multiple measures at once, centrality_load for a related measure.

Examples

adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality_betweenness(adj)

Closeness Centrality

Description

Inverse of the average shortest path distance from a node to all others. For directed networks, centrality_incloseness and centrality_outcloseness measure incoming and outgoing closeness.

Usage

centrality_closeness(x, mode = "all", ...)

centrality_incloseness(x, ...)

centrality_outcloseness(x, ...)

Arguments

Value

Named numeric vector of closeness values.

See Also

centrality for computing multiple measures at once, centrality_harmonic for a variant that handles disconnected graphs.

Examples

adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality_closeness(adj)

Burt's Constraint

Description

Network constraint measuring the extent to which a node's connections are redundant. Low constraint indicates access to structural holes (brokerage opportunities).

Usage

centrality_constraint(x, ...)

Arguments

Value

Named numeric vector of constraint values.

See Also

centrality for computing multiple measures at once.

Examples

adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality_constraint(adj)

K-Core Decomposition (Coreness)

Description

Assigns each node to its maximum k-core. A k-core is a maximal subgraph where every node has at least k connections within the subgraph.

Usage

centrality_coreness(x, mode = "all", ...)

Arguments

Value

Named numeric vector of coreness values.

See Also

centrality for computing multiple measures at once.

Examples

adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality_coreness(adj)

Current Flow Betweenness Centrality

Description

Betweenness based on electrical current flow rather than shortest paths. Uses the Laplacian pseudoinverse. Requires a connected graph.

Usage

centrality_current_flow_betweenness(x, ...)

Arguments

Value

Named numeric vector of current flow betweenness values.

See Also

centrality for computing multiple measures at once, centrality_betweenness for the shortest-path variant.

Examples

adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality_current_flow_betweenness(adj)

Current Flow Closeness Centrality

Description

Information centrality based on electrical current flow through the network. Uses the pseudoinverse of the Laplacian matrix. Requires a connected graph.

Usage

centrality_current_flow_closeness(x, ...)

Arguments

Value

Named numeric vector of current flow closeness values.

See Also

centrality for computing multiple measures at once, centrality_closeness for the shortest-path variant.

Examples

adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality_current_flow_closeness(adj)

Degree Centrality

Description

Number of edges connected to each node. For directed networks, centrality_indegree counts incoming edges and centrality_outdegree counts outgoing edges.

Usage

centrality_degree(x, mode = "all", ...)

centrality_indegree(x, ...)

centrality_outdegree(x, ...)

Arguments

Value

Named numeric vector of degree values.

See Also

centrality for computing multiple measures at once, centrality_strength for the weighted version.

Examples

adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality_degree(adj)

Diffusion Centrality

Description

Sum of scaled degrees of a node and its neighbors, measuring the node's potential for spreading information through the network.

Usage

centrality_diffusion(x, mode = "all", lambda = 1, ...)

Arguments

Value

Named numeric vector of diffusion centrality values.

See Also

centrality for computing multiple measures at once.

Examples

adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality_diffusion(adj)

Eccentricity

Description

Maximum shortest path distance from a node to any other node. For directed networks, centrality_ineccentricity and centrality_outeccentricity use incoming and outgoing paths.

Usage

centrality_eccentricity(x, mode = "all", ...)

centrality_ineccentricity(x, ...)

centrality_outeccentricity(x, ...)

Arguments

Value

Named numeric vector of eccentricity values.

See Also

centrality for computing multiple measures at once.

Examples

adj <- matrix(c(0, 1, 0, 1, 0, 1, 0, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality_eccentricity(adj)

Eigenvector Centrality

Description

Influence-based centrality where a node's score depends on the scores of its neighbors. Nodes connected to other high-scoring nodes get higher scores.

Usage

centrality_eigenvector(x, ...)

Arguments

Value

Named numeric vector of eigenvector centrality values.

See Also

centrality for computing multiple measures at once, centrality_pagerank for a random walk variant.

Examples

adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality_eigenvector(adj)

Harmonic Centrality

Description

Sum of inverse shortest path distances to all other nodes. Unlike closeness, harmonic centrality handles disconnected graphs naturally (unreachable nodes contribute 0 instead of making the measure undefined).

Usage

centrality_harmonic(x, mode = "all", ...)

centrality_inharmonic(x, ...)

centrality_outharmonic(x, ...)

Arguments

Value

Named numeric vector of harmonic centrality values.

See Also

centrality for computing multiple measures at once, centrality_closeness for the traditional variant.

Examples

adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality_harmonic(adj)

Geodesic K-Path Centrality

Description

Count of nodes reachable within shortest path distance k. Measures how many nodes a given node can reach quickly.

Usage

centrality_kreach(x, mode = "all", k = 3, ...)

Arguments

Value

Named numeric vector of k-reach centrality values.

See Also

centrality for computing multiple measures at once.

Examples

adj <- matrix(c(0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0), 4, 4)
rownames(adj) <- colnames(adj) <- c("A", "B", "C", "D")
centrality_kreach(adj, k = 2)

Laplacian Centrality

Description

Energy drop from the graph Laplacian when a node is removed (Qi et al. 2012). Measures a node's importance to the overall network energy.

Usage

centrality_laplacian(x, ...)

Arguments

Value

Named numeric vector of Laplacian centrality values.

See Also

centrality for computing multiple measures at once.

Examples

adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality_laplacian(adj)

Leverage Centrality

Description

Measures a node's influence over its neighbors based on relative degree differences. Positive values indicate the node has more connections than its average neighbor.

Usage

centrality_leverage(x, mode = "all", ...)

Arguments

Value

Named numeric vector of leverage centrality values (range -1 to 1).

See Also

centrality for computing multiple measures at once.

Examples

adj <- matrix(c(0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0), 4, 4)
rownames(adj) <- colnames(adj) <- c("A", "B", "C", "D")
centrality_leverage(adj)

Load Centrality

Description

Fraction of all shortest paths passing through a node, similar to betweenness but weighting paths by 1/count (Goh et al. 2001).

Usage

centrality_load(x, ...)

Arguments

Value

Named numeric vector of load centrality values.

See Also

centrality for computing multiple measures at once, centrality_betweenness for the standard variant.

Examples

adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality_load(adj)

PageRank Centrality

Description

Random walk centrality measuring node importance. Simulates a random walker that follows edges with probability damping and jumps to a random node with probability 1 - damping.

Usage

centrality_pagerank(x, damping = 0.85, personalized = NULL, ...)

Arguments

Value

Named numeric vector of PageRank values.

See Also

centrality for computing multiple measures at once, centrality_eigenvector for a related measure.

Examples

adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality_pagerank(adj)
centrality_pagerank(adj, damping = 0.9)

Percolation Centrality

Description

Importance for spreading processes using node states. Each node has a state (0-1) representing how activated it is. When all states are equal, equivalent to betweenness.

Usage

centrality_percolation(x, states = NULL, ...)

Arguments

Value

Named numeric vector of percolation centrality values.

See Also

centrality for computing multiple measures at once, centrality_betweenness which this generalizes.

Examples

adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality_percolation(adj)
centrality_percolation(adj, states = c(A = 0.8, B = 0.2, C = 0.5))

Bonacich Power Centrality

Description

Measures influence based on connections to other influential nodes. The power parameter controls whether connections to well-connected nodes increase or decrease centrality.

Usage

centrality_power(x, mode = "all", ...)

Arguments

Value

Named numeric vector of power centrality values.

See Also

centrality for computing multiple measures at once, centrality_eigenvector for a related measure.

Examples

adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality_power(adj)

Strength Centrality (Weighted Degree)

Description

Sum of edge weights connected to each node. For directed networks, centrality_instrength sums incoming weights and centrality_outstrength sums outgoing weights.

Usage

centrality_strength(x, mode = "all", ...)

centrality_instrength(x, ...)

centrality_outstrength(x, ...)

Arguments

Value

Named numeric vector of strength values.

See Also

centrality for computing multiple measures at once, centrality_degree for the unweighted version.

Examples

mat <- matrix(c(0, .5, .3, .5, 0, .8, .3, .8, 0), 3, 3)
rownames(mat) <- colnames(mat) <- c("A", "B", "C")
centrality_strength(mat)

Subgraph Centrality

Description

Participation in closed loops (walks), weighting shorter loops more heavily. Based on the diagonal of the matrix exponential of the adjacency matrix.

Usage

centrality_subgraph(x, ...)

Arguments

Value

Named numeric vector of subgraph centrality values.

See Also

centrality for computing multiple measures at once.

Examples

adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality_subgraph(adj)

Local Transitivity (Clustering Coefficient)

Description

Proportion of triangles around each node relative to the number of possible triangles. Measures how tightly clustered a node's neighborhood is.

Usage

centrality_transitivity(x, transitivity_type = "local", isolates = "nan", ...)

Arguments

Value

Named numeric vector of transitivity values.

See Also

centrality for computing multiple measures at once.

Examples

adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality_transitivity(adj)

VoteRank Centrality

Description

Identifies influential spreaders via an iterative voting mechanism. Returns normalized rank (1 = most influential). Based on Zhang et al. (2016).

Usage

centrality_voterank(x, ...)

Arguments

Value

Named numeric vector of VoteRank values.

See Also

centrality for computing multiple measures at once.

Examples

adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality_voterank(adj)

Check and Handle Duplicate Edges

Description

Detects duplicate edges in undirected networks and either errors with guidance or aggregates them per the user's edge_duplicates setting.

Usage

check_duplicate_edges(edges, directed, edge_duplicates)

Arguments

Value

Possibly aggregated edge data frame.

Generate Circle Vertices

Description

Generate Circle Vertices

Usage

circle_vertices(x, y, r, n = 50)

Arguments

Value

List with x, y vectors of vertices.

Generate Cloud Vertices

Description

Generate Cloud Vertices

Usage

cloud_vertices(x, y, r, n = 100)

Arguments

Value

List with x, y vectors of vertices.

Cluster Quality Metrics

Description

Computes per-cluster and global quality metrics for network partitioning. Supports both binary and weighted networks.

Usage

cluster_quality(x, clusters, weighted = TRUE, directed = TRUE)

cqual(x, clusters, weighted = TRUE, directed = TRUE)

Arguments

Value

A cluster_quality object with:

See cluster_quality.

Examples

mat <- matrix(runif(100), 10, 10)
diag(mat) <- 0
clusters <- c(1,1,1,2,2,2,3,3,3,3)

q <- cluster_quality(mat, clusters)
q$per_cluster   # Per-cluster metrics
q$global        # Modularity, coverage
mat <- matrix(runif(100), 10, 10)
diag(mat) <- 0
cqual(mat, c(1,1,1,2,2,2,3,3,3,3))

Test Significance of Community Structure

Description

Compares observed modularity against a null model distribution to assess whether the detected community structure is statistically significant.

Usage

cluster_significance(
  x,
  communities,
  n_random = 100,
  method = c("configuration", "gnm"),
  seed = NULL
)

csig(
  x,
  communities,
  n_random = 100,
  method = c("configuration", "gnm"),
  seed = NULL
)

Arguments

Details

The test works by:

1. Computing the modularity of the provided community structure

2. Generating n_random random networks using the specified null model

3. For each random network, detecting communities with Louvain and computing modularity

4. Comparing the observed modularity to this null distribution

A significant result (low p-value) indicates that the community structure is stronger than expected by chance for networks with similar properties.

Value

A cograph_cluster_significance object with:

observed_modularity

Modularity of the input communities

null_mean

Mean modularity of random networks

null_sd

Standard deviation of null modularity

z_score

Standardized score: (observed - null_mean) / null_sd

p_value

One-sided p-value (probability of observing equal or higher modularity by chance)

null_values

Vector of modularity values from null distribution

method

Null model method used

n_random

Number of random networks generated

See cluster_significance.

References

Reichardt, J., & Bornholdt, S. (2006). Statistical mechanics of community detection. Physical Review E, 74, 016110.

See Also

communities, cluster_quality

Examples

## Not run: 
if (requireNamespace("igraph", quietly = TRUE)) {
  g <- igraph::make_graph("Zachary")
  comm <- community_louvain(g)

  # Test significance
  sig <- cluster_significance(g, comm, n_random = 100, seed = 123)
  print(sig)

  # Configuration model (stricter test)
  sig2 <- cluster_significance(g, comm, method = "configuration")
}

## End(Not run)
if (requireNamespace("igraph", quietly = TRUE)) {
  g <- igraph::make_graph("Zachary")
  comm <- community_louvain(g)
  csig(g, comm, n_random = 20, seed = 1)
}

Cluster Summary Statistics

Description

Aggregates node-level network weights to cluster-level summaries. Computes both macro (cluster-to-cluster) transitions and per-cluster transitions (how nodes connect inside each cluster).

Usage

cluster_summary(
  x,
  clusters = NULL,
  method = c("sum", "mean", "median", "max", "min", "density", "geomean"),
  type = c("tna", "cooccurrence", "semi_markov", "raw"),
  directed = TRUE,
  compute_within = TRUE
)

csum(
  x,
  clusters = NULL,
  method = c("sum", "mean", "median", "max", "min", "density", "geomean"),
  type = c("tna", "cooccurrence", "semi_markov", "raw"),
  directed = TRUE,
  compute_within = TRUE
)

Arguments

Details

This is the core function for Multi-Cluster Multi-Level (MCML) analysis. Use as_tna to convert results to tna objects for further analysis with the tna package.

Workflow

Typical MCML analysis workflow:

# 1. Create network
net <- cograph(edges, nodes = nodes)
net$nodes$clusters <- group_assignments

# 2. Compute cluster summary
cs <- cluster_summary(net, type = "tna")

# 3. Convert to tna models
tna_models <- as_tna(cs)

# 4. Analyze/visualize
plot(tna_models$macro)
tna::centralities(tna_models$macro)

Between-Cluster Matrix Structure

The macro$weights matrix has clusters as both rows and columns:

• Off-diagonal (row i, col j): Aggregated weight from cluster i to cluster j

• Diagonal (row i, col i): Per-cluster total (sum of internal edges in cluster i)

When type = "tna", rows sum to 1 and diagonal values represent "retention rate" - the probability of staying inside the same cluster.

Choosing method and type

Value

A cluster_summary object (S3 class) containing:

macro

A tna object representing the macro (cluster-level) network:

weights

k x k matrix of cluster-to-cluster weights, where k is the number of clusters. Row i, column j contains the aggregated weight from cluster i to cluster j. Diagonal contains aggregated intra-cluster weight (retention / self-loops). Processing depends on type.

inits

Numeric vector of length k. Initial state distribution across clusters, computed from column sums of the original matrix. Represents the proportion of incoming edges to each cluster.

clusters

Named list with one element per cluster. Each element is a tna object containing:

weights

n_i x n_i matrix for nodes inside that cluster. Shows internal transitions between nodes in the same cluster.

inits

Initial distribution for the cluster.

NULL if compute_within = FALSE.

cluster_members

Named list mapping cluster names to their member node labels. Example: list(A = c("n1", "n2"), B = c("n3", "n4", "n5"))

meta

List of metadata:

type

The type argument used ("tna", "raw", etc.)

method

The method argument used ("sum", "mean", etc.)

directed

Logical, whether network was treated as directed

n_nodes

Total number of nodes in original network

n_clusters

Number of clusters

cluster_sizes

Named vector of cluster sizes

See cluster_summary.

See Also

as_tna to convert results to tna objects, plot_mcml for two-layer visualization, plot_mtna for flat cluster visualization

Examples

# -----------------------------------------------------
# Basic usage with matrix and cluster vector
# -----------------------------------------------------
mat <- matrix(runif(100), 10, 10)
diag(mat) <- 0
rownames(mat) <- colnames(mat) <- LETTERS[1:10]

clusters <- c(1, 1, 1, 2, 2, 2, 3, 3, 3, 3)
cs <- cluster_summary(mat, clusters)

# Access results
cs$macro$weights      # 3x3 cluster transition matrix
cs$macro$inits        # Initial distribution
cs$clusters$`1`$weights # Per-cluster 1 transitions
cs$meta               # Metadata

# -----------------------------------------------------
# Named list clusters (more readable)
# -----------------------------------------------------
clusters <- list(
  Alpha = c("A", "B", "C"),
  Beta = c("D", "E", "F"),
  Gamma = c("G", "H", "I", "J")
)
cs <- cluster_summary(mat, clusters, type = "tna")
cs$macro$weights      # Rows/cols named Alpha, Beta, Gamma
cs$clusters$Alpha     # Per-cluster Alpha network

# -----------------------------------------------------
# Auto-detect clusters from cograph_network
# -----------------------------------------------------
net <- as_cograph(mat)
net$nodes$clusters <- c(1, 1, 1, 2, 2, 2, 3, 3, 3, 3)
cs <- cluster_summary(net)  # No clusters argument needed

# -----------------------------------------------------
# Different aggregation methods
# -----------------------------------------------------
cs_sum <- cluster_summary(mat, clusters, method = "sum")   # Total flow
cs_mean <- cluster_summary(mat, clusters, method = "mean") # Average
cs_max <- cluster_summary(mat, clusters, method = "max")   # Strongest

# -----------------------------------------------------
# Raw counts vs TNA probabilities
# -----------------------------------------------------
cs_raw <- cluster_summary(mat, clusters, type = "raw")
cs_tna <- cluster_summary(mat, clusters, type = "tna")

rowSums(cs_raw$macro$weights)  # Various sums
rowSums(cs_tna$macro$weights)  # All equal to 1

# -----------------------------------------------------
# Skip within-cluster computation for speed
# -----------------------------------------------------
cs_fast <- cluster_summary(mat, clusters, compute_within = FALSE)
cs_fast$clusters  # NULL

# -----------------------------------------------------
# Convert to tna objects for tna package
# -----------------------------------------------------
cs <- cluster_summary(mat, clusters, type = "tna")
tna_models <- as_tna(cs)
# tna_models$macro         # tna object
# tna_models$Alpha         # tna object (cluster network)
mat <- matrix(c(0.5, 0.2, 0.3, 0.1, 0.6, 0.3, 0.4, 0.1, 0.5), 3, 3,
              byrow = TRUE,
              dimnames = list(c("A", "B", "C"), c("A", "B", "C")))
csum(mat, list(G1 = c("A", "B"), G2 = c("C")))

Create a Network Visualization

Description

The main entry point for cograph. Accepts adjacency matrices, edge lists, igraph, statnet network, qgraph, or tna objects and creates a visualization-ready network object.

Usage

cograph(
  input,
  layout = NULL,
  directed = NULL,
  nodes = NULL,
  seed = 42,
  simplify = FALSE,
  ...
)

Arguments

Value

A cograph_network object that can be further customized and rendered.

See Also

splot for base R graphics rendering, soplot for grid graphics rendering, sn_nodes for node customization, sn_edges for edge customization, sn_layout for changing layouts, sn_theme for visual themes, sn_palette for color palettes, from_qgraph and from_tna for converting external objects

Examples

# From adjacency matrix (no layout computed yet - fast!)
adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), nrow = 3)
net <- cograph(adj)

# Layout computed automatically when plotting
splot(net)  # Uses spring layout by default

# From edge list
edges <- data.frame(from = c(1, 1, 2), to = c(2, 3, 3))
cograph(edges)

# Compute layout immediately if needed
cograph(adj, layout = "circle") |> splot()

# With customization (pipe-friendly workflow)
adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), nrow = 3)
cograph(adj) |>
  sn_nodes(fill = "steelblue") |>
  sn_edges(color = "gray50") |>
  splot(layout = "circle")

# Weighted network with automatic styling
w_adj <- matrix(c(0, 0.5, -0.3, 0.5, 0, 0.4, -0.3, 0.4, 0), nrow = 3)
cograph(w_adj) |>
  sn_edges(color = "weight", width = "weight") |>
  splot()

# With igraph (if installed)
if (requireNamespace("igraph", quietly = TRUE)) {
  g <- igraph::make_ring(10)
  cograph(g) |> splot()
}

Main Entry Point

Description

The primary function for creating network visualizations.

Color Nodes by Community

Description

Generate colors for nodes based on community membership. Designed for direct use with splot() node.color parameter.

Usage

color_communities(x, method = "louvain", palette = NULL, ...)

Arguments

Value

A named character vector of colors (one per node), suitable for use with splot() node.color parameter.

See Also

detect_communities, splot

Examples

adj <- matrix(c(0, .5, .8, 0,
                .5, 0, .3, .6,
                .8, .3, 0, .4,
                 0, .6, .4, 0), 4, 4, byrow = TRUE)
rownames(adj) <- colnames(adj) <- c("A", "B", "C", "D")

# Basic usage with splot
splot(adj, node_fill = color_communities(adj))

# Custom palette
splot(adj, node_fill = color_communities(adj, palette = c("red", "blue")))

Community Detection

Description

Detects communities/clusters in networks using various algorithms. Provides a unified interface to igraph's community detection functions with full parameter exposure.

Usage

communities(
  x,
  method = c("louvain", "leiden", "fast_greedy", "walktrap", "infomap",
    "label_propagation", "edge_betweenness", "leading_eigenvector", "spinglass",
    "optimal", "fluid"),
  weights = NULL,
  resolution = 1,
  directed = NULL,
  seed = NULL,
  ...
)

Arguments

Details

Algorithm Selection Guide:

Value

A cograph_communities object (extends igraph's communities class) with components:

membership

Integer vector of community assignments

modularity

Modularity score of the partition

algorithm

Name of the algorithm used

names

Node names if available

vcount

Number of nodes

See Also

community_louvain, community_leiden, community_fast_greedy, community_walktrap, community_infomap, community_label_propagation, community_edge_betweenness, community_leading_eigenvector, community_spinglass, community_optimal, community_fluid

Examples

# Create a network with community structure
if (requireNamespace("igraph", quietly = TRUE)) {
  g <- igraph::make_graph("Zachary")

  # Default (Louvain)
  comm <- cograph::communities(g)
  print(comm)

  # Walktrap
  comm2 <- cograph::communities(g, method = "walktrap")
  print(comm2)
}

Consensus Community Detection

Description

Runs a stochastic community detection algorithm multiple times and finds consensus communities via co-occurrence matrix thresholding. This approach produces more robust and stable community assignments than single runs.

Usage

community_consensus(
  x,
  method = c("louvain", "leiden", "infomap", "label_propagation", "spinglass"),
  n_runs = 100,
  threshold = 0.5,
  seed = NULL,
  ...
)

com_consensus(
  x,
  method = c("louvain", "leiden", "infomap", "label_propagation", "spinglass"),
  n_runs = 100,
  threshold = 0.5,
  seed = NULL,
  ...
)

Arguments

Details

The algorithm works as follows:

1. Run the specified algorithm n_runs times (without seeds to allow variation)

2. Build a co-occurrence matrix counting how often each pair of nodes appears in the same community

3. Normalize to proportions (0-1)

4. Threshold to create a consensus graph (edge if co-occurrence >= threshold)

5. Run walktrap on the consensus graph to get final communities

Value

A cograph_communities object with consensus membership.

References

Lancichinetti, A., & Fortunato, S. (2012). Consensus clustering in complex networks. Scientific Reports, 2, 336.

See Also

communities, community_louvain

Examples

if (requireNamespace("igraph", quietly = TRUE)) {
  g <- igraph::make_graph("Zachary")

  # Consensus from 50 Louvain runs
  cc <- community_consensus(g, method = "louvain", n_runs = 50)
  print(cc)

  # Stricter threshold for more robust communities
  cc2 <- community_consensus(g, threshold = 0.7, n_runs = 100)
}

Edge Betweenness Community Detection

Description

Girvan-Newman algorithm. Iteratively removes edges with highest betweenness centrality to reveal community structure.

Usage

community_edge_betweenness(
  x,
  weights = NULL,
  directed = TRUE,
  edge.betweenness = TRUE,
  merges = TRUE,
  bridges = TRUE,
  modularity = TRUE,
  membership = TRUE,
  ...
)

com_eb(
  x,
  weights = NULL,
  directed = TRUE,
  edge.betweenness = TRUE,
  merges = TRUE,
  bridges = TRUE,
  modularity = TRUE,
  membership = TRUE,
  ...
)

Arguments

Value

A cograph_communities object

A cograph_communities object. See detect_communities.

References

Girvan, M., & Newman, M.E.J. (2002). Community structure in social and biological networks. PNAS, 99(12), 7821-7826.

Examples

g <- igraph::make_graph("Zachary")
comm <- community_edge_betweenness(g)
igraph::membership(comm)


net <- as_cograph(matrix(runif(25), 5, 5))
com_eb(net)

Fast Greedy Community Detection

Description

Hierarchical agglomeration using greedy modularity optimization. Produces a dendrogram of community merges.

Usage

community_fast_greedy(
  x,
  weights = NULL,
  merges = TRUE,
  modularity = TRUE,
  membership = TRUE,
  ...
)

com_fg(
  x,
  weights = NULL,
  merges = TRUE,
  modularity = TRUE,
  membership = TRUE,
  ...
)

Arguments

Value

A cograph_communities object with optional dendrogram

A cograph_communities object. See detect_communities.

References

Clauset, A., Newman, M.E.J., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70, 066111.

Examples

g <- igraph::make_graph("Zachary")
comm <- community_fast_greedy(g)
igraph::membership(comm)

Fluid Communities Detection

Description

Simulates fluid dynamics where communities compete for nodes. Requires specifying the number of communities.

Usage

community_fluid(x, no.of.communities, ...)

com_fl(x, no.of.communities, ...)

Arguments

Value

A cograph_communities object

A cograph_communities object. See detect_communities.

References

Pares, F., Gasulla, D.G., Vilalta, A., Moreno, J., Ayguade, E., Labarta, J., Cortes, U., & Suzumura, T. (2018). Fluid communities: A competitive, scalable and diverse community detection algorithm. Studies in Computational Intelligence, 689, 229-240.

Examples

if (requireNamespace("igraph", quietly = TRUE)) {
  g <- igraph::make_graph("Zachary")

  # Detect exactly 2 communities
  comm <- community_fluid(g, no.of.communities = 2)
}

m <- matrix(runif(25), 5, 5); diag(m) <- 0
net <- as_cograph(m)
com_fl(net, no.of.communities = 2)

Infomap Community Detection

Description

Information-theoretic community detection based on random walk dynamics. Minimizes the map equation (description length of random walks).

Usage

community_infomap(
  x,
  weights = NULL,
  v.weights = NULL,
  nb.trials = 10,
  modularity = TRUE,
  seed = NULL,
  ...
)

com_im(
  x,
  weights = NULL,
  v.weights = NULL,
  nb.trials = 10,
  modularity = TRUE,
  seed = NULL,
  ...
)

Arguments

Value

A cograph_communities object

A cograph_communities object. See detect_communities.

References

Rosvall, M., & Bergstrom, C.T. (2008). Maps of random walks on complex networks reveal community structure. PNAS, 105(4), 1118-1123.

Examples

if (requireNamespace("igraph", quietly = TRUE)) {
  g <- igraph::make_graph("Zachary")
  comm <- community_infomap(g, nb.trials = 20)
}

Label Propagation Community Detection

Description

Fast semi-synchronous label propagation algorithm. Each node adopts the most frequent label among its neighbors.

Usage

community_label_propagation(
  x,
  weights = NULL,
  mode = c("out", "in", "all"),
  initial = NULL,
  fixed = NULL,
  seed = NULL,
  ...
)

com_lp(
  x,
  weights = NULL,
  mode = c("out", "in", "all"),
  initial = NULL,
  fixed = NULL,
  seed = NULL,
  ...
)

Arguments

Value

A cograph_communities object

A cograph_communities object. See detect_communities.

References

Raghavan, U.N., Albert, R., & Kumara, S. (2007). Near linear time algorithm to detect community structures in large-scale networks. Physical Review E, 76, 036106.

Examples

if (requireNamespace("igraph", quietly = TRUE)) {
  g <- igraph::make_graph("Zachary")

  # Basic label propagation
  comm <- community_label_propagation(g)

  # With some nodes fixed to specific communities
  initial <- rep(NA, igraph::vcount(g))
  initial[1] <- 1  # Node 1 in community 1
  initial[34] <- 2 # Node 34 in community 2
  fixed <- !is.na(initial)
  initial[is.na(initial)] <- seq_len(sum(is.na(initial)))
  comm2 <- community_label_propagation(g, initial = initial, fixed = fixed)
}

net <- as_cograph(matrix(runif(25), 5, 5))
com_lp(net)

Leading Eigenvector Community Detection

Description

Detects communities using the leading eigenvector of the modularity matrix. Hierarchical divisive algorithm.

Usage

community_leading_eigenvector(
  x,
  weights = NULL,
  steps = -1,
  start = NULL,
  options = igraph::arpack_defaults(),
  callback = NULL,
  extra = NULL,
  env = parent.frame(),
  ...
)

com_le(
  x,
  weights = NULL,
  steps = -1,
  start = NULL,
  options = igraph::arpack_defaults(),
  callback = NULL,
  extra = NULL,
  env = parent.frame(),
  ...
)

Arguments

Value

A cograph_communities object

A cograph_communities object. See detect_communities.

References

Newman, M.E.J. (2006). Finding community structure using the eigenvectors of matrices. Physical Review E, 74, 036104.

Examples

g <- igraph::make_graph("Zachary")
comm <- community_leading_eigenvector(g)
igraph::membership(comm)


net <- as_cograph(matrix(runif(25), 5, 5))
com_le(net)

Leiden Community Detection

Description

Leiden algorithm - an improved version of Louvain that guarantees well-connected communities. Supports CPM and modularity objectives.

Usage

community_leiden(
  x,
  weights = NULL,
  resolution = 1,
  objective_function = c("CPM", "modularity"),
  beta = 0.01,
  initial_membership = NULL,
  n_iterations = 2,
  vertex_weights = NULL,
  seed = NULL,
  ...
)

com_ld(
  x,
  weights = NULL,
  resolution = 1,
  objective_function = c("CPM", "modularity"),
  beta = 0.01,
  initial_membership = NULL,
  n_iterations = 2,
  vertex_weights = NULL,
  seed = NULL,
  ...
)

Arguments

Value

A cograph_communities object

A cograph_communities object. See detect_communities.

References

Traag, V.A., Waltman, L., & van Eck, N.J. (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports, 9, 5233.

Examples

if (requireNamespace("igraph", quietly = TRUE)) {
  g <- igraph::make_graph("Zachary")

  # Standard Leiden
  comm <- community_leiden(g)

  # Higher resolution for more communities
  comm2 <- community_leiden(g, resolution = 1.5)

  # Modularity objective
  comm3 <- community_leiden(g, objective_function = "modularity")
}

Louvain Community Detection

Description

Multi-level modularity optimization using the Louvain algorithm. Fast and widely used for large networks.

Usage

community_louvain(x, weights = NULL, resolution = 1, seed = NULL, ...)

com_lv(x, weights = NULL, resolution = 1, seed = NULL, ...)

Arguments

Value

A cograph_communities object

A cograph_communities object. See detect_communities.

References

Blondel, V.D., Guillaume, J.L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics, P10008.

Examples

if (requireNamespace("igraph", quietly = TRUE)) {
  g <- igraph::make_graph("Zachary")
  comm <- community_louvain(g)
  igraph::membership(comm)

  # Reproducible result with seed
  comm1 <- community_louvain(g, seed = 42)
  comm2 <- community_louvain(g, seed = 42)
  identical(igraph::membership(comm1), igraph::membership(comm2))
}

Optimal Community Detection

Description

Finds the optimal community structure by maximizing modularity exactly. Very slow - only use for small networks (<50 nodes).

Usage

community_optimal(x, weights = NULL, ...)

com_op(x, weights = NULL, ...)

Arguments

Value

A cograph_communities object

A cograph_communities object. See detect_communities.

Note

This is an NP-hard problem. Use only for tiny networks.

References

Brandes, U., Delling, D., Gaertler, M., Gorke, R., Hoefer, M., Nikoloski, Z., & Wagner, D. (2008). On modularity clustering. IEEE Transactions on Knowledge and Data Engineering, 20(2), 172-188.

Examples

g <- igraph::make_ring(10)
comm <- community_optimal(g)
igraph::membership(comm)


net <- as_cograph(matrix(runif(25), 5, 5))
com_op(net)

Get Community Sizes

Description

Get Community Sizes

Usage

community_sizes(x)

Arguments

Value

Named integer vector of community sizes

Examples

g <- igraph::make_graph("Zachary")
comm <- community_louvain(g)
community_sizes(comm)

Spinglass Community Detection

Description

Statistical mechanics approach using simulated annealing. Can handle negative edge weights.

Usage

community_spinglass(
  x,
  weights = NULL,
  vertex = NULL,
  spins = 25,
  parupdate = FALSE,
  start.temp = 1,
  stop.temp = 0.01,
  cool.fact = 0.99,
  update.rule = c("config", "random", "simple"),
  gamma = 1,
  implementation = c("orig", "neg"),
  gamma.minus = 1,
  seed = NULL,
  ...
)

com_sg(
  x,
  weights = NULL,
  vertex = NULL,
  spins = 25,
  parupdate = FALSE,
  start.temp = 1,
  stop.temp = 0.01,
  cool.fact = 0.99,
  update.rule = c("config", "random", "simple"),
  gamma = 1,
  implementation = c("orig", "neg"),
  gamma.minus = 1,
  seed = NULL,
  ...
)

Arguments

Value

A cograph_communities object

A cograph_communities object. See detect_communities.

References

Reichardt, J., & Bornholdt, S. (2006). Statistical mechanics of community detection. Physical Review E, 74, 016110.

Examples

g <- igraph::make_graph("Zachary")
comm <- community_spinglass(g)
igraph::membership(comm)


net <- as_cograph(matrix(runif(25), 5, 5))
com_sg(net)

Walktrap Community Detection

Description

Detects communities via random walks. Nodes within the same community tend to have short random walk distances.

Usage

community_walktrap(
  x,
  weights = NULL,
  steps = 4,
  merges = TRUE,
  modularity = TRUE,
  membership = TRUE,
  ...
)

com_wt(
  x,
  weights = NULL,
  steps = 4,
  merges = TRUE,
  modularity = TRUE,
  membership = TRUE,
  ...
)

Arguments

Value

A cograph_communities object

A cograph_communities object. See detect_communities.

References

Pons, P., & Latapy, M. (2006). Computing communities in large networks using random walks. Journal of Graph Algorithms and Applications, 10(2), 191-218.

Examples

if (requireNamespace("igraph", quietly = TRUE)) {
  g <- igraph::make_graph("Zachary")

  # Default 4 steps
  comm <- community_walktrap(g)

  # More steps for larger communities
  comm2 <- community_walktrap(g, steps = 8)
}

Compare Community Structures

Description

Compares two community structures using various similarity measures.

Usage

compare_communities(
  comm1,
  comm2,
  method = c("vi", "nmi", "split.join", "rand", "adjusted.rand")
)

Arguments

Value

Numeric similarity/distance value

Examples

if (requireNamespace("igraph", quietly = TRUE)) {
  g <- igraph::make_graph("Zachary")
  c1 <- community_louvain(g)
  c2 <- community_leiden(g)
  compare_communities(c1, c2, "nmi")
}

Compute Adaptive Base Edge Size

Description

Calculates the maximum edge width that decreases with more nodes. Inspired by qgraph but scaled for line widths (not pixels).

Usage

compute_adaptive_esize(n_nodes, directed = FALSE)

Arguments

Details

The formula produces reasonable line widths:

• 3 nodes: ~5

• 10 nodes: ~4.5

• 50 nodes: ~3

• 100 nodes: ~2

• 200 nodes: ~1.2

For directed networks, the size is reduced by 30% (minimum 1).

Value

Numeric maximum edge width (suitable for lwd parameter).

Compute Circular Layout

Description

Positions nodes along arcs of a circle, with each group occupying one arc. Groups are separated by gaps controlled by angle_spacing.

Usage

compute_circular_layout(
  node_list,
  lab,
  group_indices,
  n_groups,
  angle_spacing = 0.15,
  scale = 1
)

Arguments

Value

List with x and y position vectors.

Compute Connectivity-Based Jitter (Horizontal Layout)

Description

For horizontal layouts (left/right columns). Nodes with more cross-group connections are jittered horizontally toward center.

Usage

compute_connectivity_jitter_horizontal(
  weights,
  g1_idx,
  g2_idx,
  amount = 0.8,
  side = "group1"
)

Arguments

Value

Numeric vector of x-offsets for each node.

Compute Connectivity-Based Jitter (Vertical Layout)

Description

For vertical layouts (top/bottom rows). Nodes with more cross-group connections are jittered vertically toward center.

Usage

compute_connectivity_jitter_vertical(
  weights,
  g1_idx,
  g2_idx,
  amount = 0.8,
  side = "group1"
)

Arguments

Value

Numeric vector of y-offsets for each node.

Compute Edge Curvatures

Description

Determines per-edge curvature values based on reciprocal-edge detection, curve mode, and layout geometry.

Usage

compute_edge_curvatures(curvature, curves, edges, layout_mat)

Arguments

Value

List with curves_vec, is_reciprocal.

Wrapper for Gephi FR Layout (for layout registry)

Description

Wrapper for Gephi FR Layout (for layout registry)

Usage

compute_layout_gephi_fr(
  network,
  area = 10000,
  gravity = 1,
  speed = 1,
  niter = 100,
  seed = NULL,
  initial = NULL,
  normalize = TRUE,
  gravity_mode = "linear",
  cooling_mode = "constant",
  anchor_strength = 0,
  ...
)

Arguments

Value

Data frame with x, y coordinates.

Compute Plot Limits

Description

Calculates xlim/ylim accounting for node radii, self-loop extents, and layout margin padding.

Usage

compute_plot_limits(
  layout_mat,
  vsize_usr,
  layout_margin,
  edges,
  n_edges,
  loop_rotations
)

Arguments

Value

List with xlim and ylim.

Compute Polygon Layout

Description

Positions nodes along edges of a regular n-sided polygon. Each group is placed along one edge. Edges are offset outward from vertices to create empty angles at corners.

Usage

compute_polygon_layout(
  node_list,
  lab,
  group_indices,
  n_sides,
  angle_spacing = 0.15,
  scale = 1
)

Arguments

Value

List with x and y position vectors.

Create Edge Data Frame

Description

Create Edge Data Frame

Usage

create_edges_df(from, to, weight = NULL, directed = FALSE)

Arguments

Value

Data frame with edge information.

Create Grid Grob Tree

Description

Create a complete grid grob tree for the network (without drawing).

Usage

create_grid_grob(network, title = NULL, background = "white")

Arguments

Value

A grid gTree object.

Create Node Data Frame

Description

Create Node Data Frame

Usage

create_nodes_df(n, labels = NULL, names = NULL)

Arguments

Value

Data frame with node information.

Generate Cross/Plus Vertices

Description

Generate Cross/Plus Vertices

Usage

cross_vertices(x, y, r, thickness = 0.3)

Arguments

Value

List with x, y vectors of vertices.

Calculate Control Point for Curved Edge

Description

Calculate Control Point for Curved Edge

Usage

curve_control_point(x1, y1, x2, y2, curvature, pivot = 0.5, shape = 0)

Arguments

Value

List with x, y coordinates of control point.

Degree Distribution Visualization

Description

Creates a histogram showing the degree distribution of a network. Useful for understanding the connectivity patterns and identifying whether a network follows particular degree distributions (e.g., power-law, normal).

Usage

degree_distribution(
  x,
  mode = "all",
  directed = NULL,
  loops = TRUE,
  simplify = "sum",
  cumulative = FALSE,
  main = "Degree Distribution",
  xlab = "Degree",
  ylab = "Frequency",
  col = "steelblue",
  ...
)

Arguments

Value

Invisibly returns the histogram object from graphics::hist().

Examples

# Basic usage
adj <- matrix(c(0, 1, 1, 0,
                1, 0, 1, 1,
                1, 1, 0, 1,
                0, 1, 1, 0), 4, 4, byrow = TRUE)
cograph::degree_distribution(adj)

# Cumulative distribution
cograph::degree_distribution(adj, cumulative = TRUE)

# For directed networks
directed_adj <- matrix(c(0, 1, 0, 0,
                         0, 0, 1, 0,
                         1, 0, 0, 1,
                         0, 1, 0, 0), 4, 4, byrow = TRUE)
cograph::degree_distribution(directed_adj, mode = "in",
  main = "In-Degree Distribution")

# With igraph
if (requireNamespace("igraph", quietly = TRUE)) {
  g <- igraph::erdos.renyi.game(100, 0.1)
  cograph::degree_distribution(g, col = "coral")
}

Detect Communities in a Network

Description

Detects communities (clusters) in a network using various community detection algorithms. Returns a data frame with node-community assignments.

Usage

detect_communities(x, method = "louvain", directed = NULL, weights = TRUE)

Arguments

Value

A data frame with columns:

• node: Node labels/names

• community: Integer community membership

Examples

# Basic usage
adj <- matrix(c(0, .5, .8, 0,
                .5, 0, .3, .6,
                .8, .3, 0, .4,
                 0, .6, .4, 0), 4, 4, byrow = TRUE)
rownames(adj) <- colnames(adj) <- c("A", "B", "C", "D")
detect_communities(adj)

# Different algorithm
detect_communities(adj, method = "walktrap")

Detect Duplicate Edges in Undirected Network

Description

Identifies edges that appear multiple times between the same pair of nodes. For undirected networks, edges A->B and B->A are considered duplicates. For directed networks, only identical from->to pairs are duplicates.

Usage

detect_duplicate_edges(edges)

Arguments

Details

This function is useful for cleaning network data before visualization. Duplicate edges can arise from:

• Data collection errors (same edge recorded twice)

• Combining multiple data sources

• Converting from formats that allow multi-edges

• Edge lists that include both A->B and B->A for undirected networks

The function creates canonical keys by sorting node pairs (lower index first), so edges 1->2 and 2->1 map to the same key "1-2" in undirected mode.

Value

A list with two components:

has_duplicates

Logical indicating whether any duplicates were found.

info

A list of duplicate details, where each element contains: nodes (the node pair), count (number of edges), and weights (vector of weights if available).

See Also

aggregate_duplicate_edges for combining duplicates into single edges

Generate Diamond Vertices

Description

Generate Diamond Vertices

Usage

diamond_vertices(x, y, r)

Arguments

Value

List with x, y vectors of vertices.

Disparity Filter for Network Backbone Extraction

Description

Implements the disparity filter (Serrano et al., 2009) to extract the statistically significant backbone of a weighted network.

Disparity Filter

Description

Extracts the statistically significant backbone of a weighted network using the disparity filter method (Serrano, Boguna, & Vespignani, 2009).

Usage

disparity_filter(x, level = 0.05, ...)

## Default S3 method:
disparity_filter(x, level = 0.05, ...)

## S3 method for class 'matrix'
disparity_filter(x, level = 0.05, ...)

## S3 method for class 'tna'
disparity_filter(x, level = 0.05, ...)

## S3 method for class 'cograph_network'
disparity_filter(x, level = 0.05, ...)

## S3 method for class 'igraph'
disparity_filter(x, level = 0.05, ...)

Arguments

Details

The disparity filter identifies edges that carry a disproportionate fraction of a node's total weight, based on a null model where weights are distributed uniformly at random.

For each node i with degree k_i, and each edge (i,j) with normalized weight p_{ij} = w_{ij} / s_i (where s_i is the node's strength), the p-value is:

p = (1 - p_{ij})^{(k_i - 1)}

Edges are significant if p < level for either endpoint.

Value

For matrices: a binary matrix (0/1) indicating significant edges. For tna, cograph_network, and igraph objects: a tna_disparity object containing the significance matrix, original weights, filtered weights, and summary statistics.

References

Serrano, M. A., Boguna, M., & Vespignani, A. (2009). Extracting the multiscale backbone of complex weighted networks. Proceedings of the National Academy of Sciences, 106(16), 6483-6488.

See Also

bootstrap for bootstrap-based significance testing

Examples

# Create a weighted network
mat <- matrix(c(
  0.0, 0.5, 0.1, 0.0,
  0.3, 0.0, 0.4, 0.1,
  0.1, 0.2, 0.0, 0.5,
  0.0, 0.1, 0.3, 0.0
), nrow = 4, byrow = TRUE)
rownames(mat) <- colnames(mat) <- c("A", "B", "C", "D")

# Extract backbone at 5% significance level
backbone <- disparity_filter(mat, level = 0.05)
backbone

# More stringent filter (1% level)
backbone_strict <- disparity_filter(mat, level = 0.01)

Draw Arrow Head

Description

Draws a filled triangular arrow head at the specified position.

Usage

draw_arrow_base(
  x,
  y,
  angle,
  size,
  arrow_angle = pi/6,
  col = "black",
  border = NULL,
  lwd = 1
)

Arguments

Draw Brain Node

Description

Simplified brain outline using overlapping curves.

Usage

draw_brain(x, y, size, fill, border_color, border_width, alpha = 1, ...)

Draw Chip Node

Description

Square with pins extending from all edges (processor/IC chip).

Usage

draw_chip(
  x,
  y,
  size,
  fill,
  border_color,
  border_width,
  alpha = 1,
  pins_per_side = 3,
  ...
)

Arguments

Draw Chip Node (Base R)

Description

Square with pins extending from all edges.

Usage

draw_chip_node_base(
  x,
  y,
  size,
  col,
  border.col,
  border.width,
  pins_per_side = 3
)

Draw Circle Node

Description

Draw Circle Node

Usage

draw_circle(x, y, size, fill, border_color, border_width, alpha = 1, ...)

Draw Circle Arrow (Dot)

Description

Draws a circular dot at the arrow position (alternative to triangular arrow).

Usage

draw_circle_arrow_base(x, y, size, col = "black", border = NULL)

Arguments

Draw Cloud Node

Description

Cloud shape (cloud computing).

Usage

draw_cloud(x, y, size, fill, border_color, border_width, alpha = 1, ...)

Draw Cross/Plus Node

Description

Draw Cross/Plus Node

Usage

draw_cross(
  x,
  y,
  size,
  fill,
  border_color,
  border_width,
  alpha = 1,
  thickness = 0.3,
  ...
)

Draw Curve with Optional Start Segment

Description

Draws a curve (as lines) with an optional differently-styled start segment. Used internally to support dashed/dotted start segments for edge direction clarity.

Usage

draw_curve_with_start_segment(
  x,
  y,
  col,
  lwd,
  lty,
  start_lty = 1,
  start_fraction = 0
)

Arguments

Draw Curved Arrow Head

Description

Draws an arrow head at the end of a curved edge, with angle following the curve direction.

Usage

draw_curved_arrow_base(
  spline_x,
  spline_y,
  size,
  arrow_angle = pi/6,
  col = "black",
  border = NULL
)

Arguments

Draw Curved Edge

Description

Draw Curved Edge

Usage

draw_curved_edge(
  x1,
  y1,
  x2,
  y2,
  curvature,
  color,
  width,
  lty,
  show_arrow,
  arrow_size,
  bidirectional = FALSE,
  curve_shape = 0,
  curve_pivot = 0.5,
  x_scale = 1,
  y_scale = 1
)

Draw Curved Edge with xspline (qgraph-style)

Description

Renders a curved edge using xspline() with optional arrow. Uses qgraph-style curve calculation for smooth, natural-looking curves. Curve direction is normalized so positive curve always bends the same visual direction regardless of edge orientation.

Usage

draw_curved_edge_base(
  x1,
  y1,
  x2,
  y2,
  curve = 0.2,
  curvePivot = 0.5,
  col = "gray50",
  lwd = 1,
  lty = 1,
  arrow = TRUE,
  asize = 0.02,
  bidirectional = FALSE,
  start_lty = 1,
  start_fraction = 0,
  arrow_angle = pi/6
)

Arguments

Draw Database Node

Description

Cylinder shape (data storage).

Usage

draw_database(x, y, size, fill, border_color, border_width, alpha = 1, ...)

Draw Database Node (Base R)

Description

Cylinder shape.

Usage

draw_database_node_base(x, y, size, col, border.col, border.width)

Draw Diamond Node

Description

Draw Diamond Node

Usage

draw_diamond(x, y, size, fill, border_color, border_width, alpha = 1, ...)

Draw Donut Node

Description

Draw a donut chart node showing a fill proportion (0-1) as an arc. The fill starts from 12 o'clock (top) and fills clockwise.

Usage

draw_donut(
  x,
  y,
  size,
  fill,
  border_color,
  border_width,
  alpha = 1,
  values = NULL,
  colors = NULL,
  inner_ratio = 0.5,
  bg_color = "gray90",
  show_value = TRUE,
  value_size = 8,
  value_color = "black",
  value_fontface = "bold",
  value_fontfamily = "sans",
  value_digits = 2,
  value_prefix = "",
  value_suffix = "",
  value_format = NULL,
  donut_border_width = NULL,
  ...
)

Arguments

Draw Donut Chart Node

Description

Renders a node as a donut chart with an inner hole. The donut shows a fill proportion (0-1) as an arc starting from 12 o'clock.

Usage

draw_donut_node_base(
  x,
  y,
  size,
  values,
  colors = NULL,
  default_color = NULL,
  inner_ratio = 0.5,
  bg_color = "gray90",
  center_color = "white",
  border.col = "black",
  border.width = 1,
  donut_border.width = NULL,
  outer_border.col = NULL,
  border.lty = 1,
  show_value = TRUE,
  value_cex = 0.8,
  value_col = "black",
  value_fontface = "bold",
  value_fontfamily = "sans",
  value_digits = 2,
  value_prefix = "",
  value_suffix = ""
)

Arguments

Draw Donut with Inner Pie Node

Description

Draw a node with an outer donut ring showing a proportion and an inner pie chart with multiple segments.

Usage

draw_donut_pie(
  x,
  y,
  size,
  fill,
  border_color,
  border_width,
  alpha = 1,
  donut_value = NULL,
  pie_values = NULL,
  pie_colors = NULL,
  inner_ratio = 0.5,
  bg_color = "gray90",
  pie_border_width = NULL,
  donut_border_width = NULL,
  ...
)

Arguments

Draw Donut with Inner Pie

Description

Renders a node with outer donut ring and inner pie chart.

Usage

draw_donut_pie_node_base(
  x,
  y,
  size,
  donut_value = 1,
  donut_color = "#4A90D9",
  pie_values = NULL,
  pie_colors = NULL,
  pie_default_color = NULL,
  inner_ratio = 0.5,
  bg_color = "gray90",
  border.col = "black",
  border.width = 1,
  pie_border.width = NULL,
  donut_border.width = NULL
)

Arguments

Draw Double Donut with Inner Pie Node

Description

Draw a node with two concentric donut rings and an optional inner pie chart. From outside to inside: outer donut ring, inner donut ring, center pie.

Usage

draw_double_donut_pie(
  x,
  y,
  size,
  fill,
  border_color,
  border_width,
  alpha = 1,
  donut_values = NULL,
  donut_colors = NULL,
  donut2_values = NULL,
  donut2_colors = NULL,
  pie_values = NULL,
  pie_colors = NULL,
  outer_inner_ratio = 0.7,
  inner_inner_ratio = 0.4,
  bg_color = "gray90",
  pie_border_width = NULL,
  donut_border_width = NULL,
  ...
)

Arguments

Draw Double Donut with Inner Pie

Description

Renders a node with two concentric donut rings and an optional inner pie chart. From outside to inside: outer donut ring, inner donut ring, center pie.

Usage

draw_double_donut_pie_node_base(
  x,
  y,
  size,
  donut_values = NULL,
  donut_colors = NULL,
  donut2_values = NULL,
  donut2_colors = NULL,
  pie_values = NULL,
  pie_colors = NULL,
  pie_default_color = NULL,
  outer_inner_ratio = 0.7,
  inner_inner_ratio = 0.4,
  bg_color = "gray90",
  border.col = "black",
  border.width = 1,
  pie_border.width = NULL,
  donut_border.width = NULL
)

Arguments

Draw Edge Label

Description

Renders a label on an edge.

Usage

draw_edge_label_base(
  x,
  y,
  label,
  cex = 0.8,
  col = "gray30",
  bg = "white",
  font = 1,
  shadow = FALSE,
  shadow_color = "gray40",
  shadow_offset = 0.5,
  shadow_alpha = 0.5
)

Arguments

Draw Ellipse Node

Description

Draw Ellipse Node

Usage

draw_ellipse(
  x,
  y,
  size,
  fill,
  border_color,
  border_width,
  alpha = 1,
  aspect = 0.6,
  ...
)

Draw Gear Node

Description

Gear/cog shape (processing/automation).

Usage

draw_gear(
  x,
  y,
  size,
  fill,
  border_color,
  border_width,
  alpha = 1,
  n_teeth = 8,
  ...
)

Arguments

Draw Heart Node

Description

Draw Heart Node

Usage

draw_heart(x, y, size, fill, border_color, border_width, alpha = 1, ...)

Draw Hexagon Node

Description

Draw Hexagon Node

Usage

draw_hexagon(x, y, size, fill, border_color, border_width, alpha = 1, ...)

Draw Network Node

Description

Interconnected nodes pattern (mini network inside).

Usage

draw_network(x, y, size, fill, border_color, border_width, alpha = 1, ...)

Draw Network Node (Base R)

Description

Interconnected nodes pattern.

Usage

draw_network_node_base(x, y, size, col, border.col, border.width)

Draw Neural Node

Description

Circle with small connection circles around the perimeter (neuron-like).

Usage

draw_neural(
  x,
  y,
  size,
  fill,
  border_color,
  border_width,
  alpha = 1,
  n_connections = 6,
  ...
)

Arguments

Draw Neural Node (Base R)

Description

Circle with small connection circles around perimeter.

Usage

draw_neural_node_base(
  x,
  y,
  size,
  col,
  border.col,
  border.width,
  n_connections = 6
)

Draw a Single Node

Description

Renders a node at the specified position with given aesthetics.

Usage

draw_node_base(
  x,
  y,
  size,
  size2 = NULL,
  shape = "circle",
  col = "#4A90D9",
  border.col = "#2C5AA0",
  border.width = 1,
  ...
)

Arguments

Draw Node Label

Description

Renders a text label at or near a node.

Usage

draw_node_label_base(
  x,
  y,
  label,
  cex = 1,
  col = "black",
  font = 1,
  family = "sans",
  hjust = 0.5,
  vjust = 0.5,
  srt = 0,
  pos = NULL,
  offset = 0.5
)

Arguments

Draw Open Arrow Head

Description

Draws an open (unfilled) V-shaped arrow head.

Usage

draw_open_arrow_base(
  x,
  y,
  angle,
  size,
  arrow_angle = pi/6,
  col = "black",
  lwd = 1
)

Arguments

Draw Pentagon Node

Description

Draw Pentagon Node

Usage

draw_pentagon(x, y, size, fill, border_color, border_width, alpha = 1, ...)

Draw Pie Node

Description

Draw a pie chart node with multiple segments.

Usage

draw_pie(
  x,
  y,
  size,
  fill,
  border_color,
  border_width,
  alpha = 1,
  values = NULL,
  colors = NULL,
  pie_border_width = NULL,
  default_color = NULL,
  ...
)

Arguments

Draw Pie Chart Node

Description

Renders a node as a pie chart with multiple colored segments. The pie is drawn slightly inside the node boundary to leave room for arrows.

Usage

draw_pie_node_base(
  x,
  y,
  size,
  values,
  colors = NULL,
  default_color = NULL,
  border.col = "black",
  border.width = 1,
  pie_border.width = NULL
)

Arguments

Draw Polygon Donut Node

Description

Draws a donut ring on a polygon shape where segments follow polygon edges. The fill shows a proportion (0-1) as filled segments starting from the top vertex.

Usage

draw_polygon_donut(
  x,
  y,
  size,
  fill,
  border_color,
  border_width,
  alpha = 1,
  values = NULL,
  colors = NULL,
  inner_ratio = 0.5,
  bg_color = "gray90",
  donut_shape = "square",
  show_value = TRUE,
  value_size = 8,
  value_color = "black",
  value_fontface = "bold",
  value_fontfamily = "sans",
  value_digits = 2,
  value_prefix = "",
  value_suffix = "",
  value_format = NULL,
  donut_border_width = NULL,
  ...
)

Arguments

Draw Polygon Donut Node (Base R)

Description

Renders a donut on a polygon shape where segments follow polygon edges. The donut shows a fill proportion (0-1) as filled segments starting from the top.

Usage

draw_polygon_donut_node_base(
  x,
  y,
  size,
  values,
  colors = NULL,
  default_color = NULL,
  inner_ratio = 0.5,
  bg_color = "gray90",
  center_color = "white",
  donut_shape = "square",
  border.col = "black",
  border.width = 1,
  donut_border.width = NULL,
  outer_border.col = NULL,
  border.lty = 1,
  show_value = TRUE,
  value_cex = 0.8,
  value_col = "black",
  value_fontface = "bold",
  value_fontfamily = "sans",
  value_digits = 2,
  value_prefix = "",
  value_suffix = ""
)

Arguments

Draw Polygon Donut with Inner Pie

Description

Renders a polygon-shaped donut node with a circular pie chart inside. Supports hexagon, square, diamond, triangle, pentagon shapes for the outer ring.

Usage

draw_polygon_donut_pie_node_base(
  x,
  y,
  size,
  donut_value = 1,
  donut_color = "#4A90D9",
  donut_shape = "hexagon",
  pie_values = NULL,
  pie_colors = NULL,
  pie_default_color = NULL,
  inner_ratio = 0.5,
  bg_color = "gray90",
  border.col = "black",
  border.width = 1,
  pie_border.width = NULL,
  donut_border.width = NULL
)

Arguments

Draw Robot Node

Description

Rounded square with antenna and eyes (robot head).

Usage

draw_robot(x, y, size, fill, border_color, border_width, alpha = 1, ...)

Draw Robot Node (Base R)

Description

Rounded square with antenna and eyes.

Usage

draw_robot_node_base(x, y, size, col, border.col, border.width)

Draw Self-Loop

Description

Draw Self-Loop

Usage

draw_self_loop(x, y, node_size, color, width, lty, rotation = pi/2)

Arguments

Draw Self-Loop Edge (qgraph-style)

Description

Renders a self-loop (edge from node to itself) using a teardrop/circular loop shape similar to qgraph.

Usage

draw_self_loop_base(
  x,
  y,
  node_size,
  col = "gray50",
  lwd = 1,
  lty = 1,
  rotation = pi/2,
  arrow = TRUE,
  asize = 0.02,
  arrow_angle = pi/6
)

Arguments

Draw Square Node

Description

Draw Square Node

Usage

draw_square(x, y, size, fill, border_color, border_width, alpha = 1, ...)

Draw Star Node

Description

Draw Star Node

Usage

draw_star(
  x,
  y,
  size,
  fill,
  border_color,
  border_width,
  alpha = 1,
  n_points = 5,
  inner_ratio = 0.4,
  ...
)

Draw Straight Edge

Description

Draw Straight Edge

Usage

draw_straight_edge(
  x1,
  y1,
  x2,
  y2,
  color,
  width,
  lty,
  show_arrow,
  arrow_size,
  bidirectional = FALSE,
  x_scale = 1,
  y_scale = 1
)

Draw Straight Edge

Description

Renders a straight edge between two points with optional arrow.

Usage

draw_straight_edge_base(
  x1,
  y1,
  x2,
  y2,
  col = "gray50",
  lwd = 1,
  lty = 1,
  arrow = TRUE,
  asize = 0.02,
  bidirectional = FALSE,
  start_lty = 1,
  start_fraction = 0,
  arrow_angle = pi/6
)

Arguments