Input Formats

Overview

cograph accepts network data in all common formats used in R. Pass any supported object directly to splot() and it will be automatically parsed.

Supported Formats

1. Adjacency/Weight Matrices

A square numeric matrix where M[i,j] represents the edge weight from node i to node j.

Sources:

cor(), cov() — correlation and covariance matrices
qgraph::getWmat() — extract weights from qgraph objects
bootnet::estimateNetwork() — network estimation output
psychonetrics — structural equation modeling networks
markovchain — transition probability matrices
as.matrix(dist()) — distance/dissimilarity matrices

Auto-detection:

Symmetric matrices are treated as undirected (upper triangle only)
Asymmetric matrices are treated as directed (all entries)
Node labels extracted from rownames()/colnames()

Usage:

# Create a weighted adjacency matrix
adj_matrix <- matrix(
  c(0, 0.8, 0.5, 0.2,
    0.8, 0, 0.6, 0,
    0.5, 0.6, 0, 0.7,
    0.2, 0, 0.7, 0),
  nrow = 4, byrow = TRUE,
  dimnames = list(c("A", "B", "C", "D"), c("A", "B", "C", "D"))
)

# Plot directly from matrix
splot(adj_matrix, title = "From Adjacency Matrix")


# Create a directed (asymmetric) matrix
directed_matrix <- matrix(
  c(0, 0.9, 0, 0,
    0.2, 0, 0.7, 0,
    0.5, 0, 0, 0.8,
    0, 0.3, 0.4, 0),
  nrow = 4, byrow = TRUE,
  dimnames = list(c("A", "B", "C", "D"), c("A", "B", "C", "D"))
)

# Directed networks detected automatically
splot(directed_matrix, title = "Directed Network (auto-detected)")

2. Edge List Data Frames

A data frame where each row is an edge with source, target, and optional weight columns.

Sources:

Database exports (SQL joins on relationship tables)
CSV files from SNAP, Pajek, KONECT repositories
igraph::as_data_frame(g, what = "edges")
API responses from social platforms
Parsed log files

Column detection (case-insensitive):

Purpose	Recognized names
Source	`from`, `source`, `src`, `v1`, `node1`, `i`
Target	`to`, `target`, `tgt`, `v2`, `node2`, `j`
Weight	`weight`, `w`, `value`, `strength`

Falls back to columns 1 and 2 if no match.

Usage:

# Create an edge list data frame
edges <- data.frame(
  from = c("Alice", "Alice", "Bob", "Bob", "Carol", "Dave"),
  to = c("Bob", "Carol", "Carol", "Dave", "Dave", "Alice"),
  weight = c(0.9, 0.5, 0.7, 0.3, 0.8, 0.4)
)

print(edges)
#>    from    to weight
#> 1 Alice   Bob    0.9
#> 2 Alice Carol    0.5
#> 3   Bob Carol    0.7
#> 4   Bob  Dave    0.3
#> 5 Carol  Dave    0.8
#> 6  Dave Alice    0.4

# Plot from edge list
splot(edges, title = "From Edge List")


# Alternative column names work too
edges_alt <- data.frame(
  source = c("X", "X", "Y", "Z"),
  target = c("Y", "Z", "Z", "X"),
  value = c(1, 0.5, 0.8, 0.3)
)
splot(edges_alt, title = "Alternative Column Names")

3. igraph Objects

Objects of class igraph from the igraph package.

Sources:

graph_from_data_frame(), graph_from_adjacency_matrix()
make_graph("Zachary"), make_ring(), sample_pa(), etc.
read_graph() — import from GraphML, GML, Pajek, edge lists
intergraph::asIgraph() — convert from network objects

Preserved:

Vertex attributes: V(g)$name, V(g)$color, etc.
Edge attributes: E(g)$weight, E(g)$type, etc.
Directedness from is_directed(g)

Usage:

# Create igraph objects
g_ring <- igraph::make_ring(6)
igraph::V(g_ring)$name <- LETTERS[1:6]

splot(g_ring, title = "igraph Ring Graph")


# Famous graph with vertex names
g_zachary <- igraph::make_graph("Zachary")
splot(g_zachary, title = "Zachary Karate Club")


# Weighted graph
g_weighted <- igraph::graph_from_adjacency_matrix(
  adj_matrix,
  mode = "undirected",
  weighted = TRUE
)
splot(g_weighted, title = "Weighted igraph")

4. network Objects (statnet)

Objects of class network from the network/statnet ecosystem.

Sources:

network::network() — create from matrix or edge list
ergm package — exponential random graph models
sna package — social network analysis
intergraph::asNetwork() — convert from igraph
Import from Pajek, UCINET formats

Preserved:

Vertex attributes via %v% operator
Edge attributes via %e% operator
Directedness from is.directed()

Usage:

# Create a network object
net_obj <- network::network(adj_matrix, directed = FALSE)

splot(net_obj, title = "From statnet network")

5. qgraph Objects

Objects created by the qgraph package.

Sources:

qgraph::qgraph(..., DoNotPlot = TRUE)
bootnet::estimateNetwork() results
psychonetrics model outputs

Preserved:

Layout coordinates from q$layout
Edge weights from q$Edgelist
Node labels from q$graphAttributes$Nodes

Usage:

# Create a qgraph object (without plotting)
q <- qgraph::qgraph(adj_matrix, DoNotPlot = TRUE)

# Plot with cograph (preserves layout)
splot(q, title = "From qgraph Object")

6. TNA Objects

Objects of class tna from the tna package (Transition Network Analysis).

Sources:

tna::tna() — build from sequence data
tna::group_tna() — grouped transition analysis
Learning analytics, process mining, behavioral sequences

Extracted:

Transition matrix from $weights
State labels from $labels
Initial probabilities from $inits

Usage:

library(tna)

# Build TNA model from included dataset
tna_model <- tna(group_regulation)

# Plot TNA model directly
splot(tna_model, title = "From TNA Model")


# With donut nodes showing initial probabilities
splot(tna_model,
      node_shape = "donut",
      donut_fill = tna_model$inits,
      title = "TNA with Initial Probabilities")

Weight Preprocessing

Parameter	Effect
`weight_digits = 2`	Round weights; edges rounding to zero are removed
`threshold = 0.3`	Remove edges with \|weight\| < threshold
`minimum = 0.3`	Alias for threshold (qgraph compatibility)
`maximum = 1.0`	Set reference maximum for edge width scaling
`edge_scale_mode`	`"linear"`, `"sqrt"`, `"log"`, or `"rank"`

# Create a network with varying weights
weights_matrix <- matrix(
  c(0, 0.1, 0.5, 0.9,
    0.1, 0, 0.2, 0.7,
    0.5, 0.2, 0, 0.3,
    0.9, 0.7, 0.3, 0),
  nrow = 4, byrow = TRUE,
  dimnames = list(LETTERS[1:4], LETTERS[1:4])
)

# Apply threshold to remove weak edges
splot(weights_matrix, threshold = 0.4, title = "Threshold = 0.4 (weak edges removed)")

Special Cases

Feature	Behavior
Negative weights	Colored differently (green/red by default)
Self-loops	Rendered as loops; control angle with `loop_rotation`
Reciprocal edges	Curved apart automatically in directed networks
Duplicate edges	Error by default; use `edge_duplicates` to aggregate

# Network with negative weights (e.g., correlation matrix)
cor_matrix <- matrix(
  c(1, 0.8, -0.5, 0.3,
    0.8, 1, 0.2, -0.7,
    -0.5, 0.2, 1, 0.4,
    0.3, -0.7, 0.4, 1),
  nrow = 4, byrow = TRUE,
  dimnames = list(LETTERS[1:4], LETTERS[1:4])
)
diag(cor_matrix) <- 0

splot(cor_matrix, title = "Negative Weights (red = negative)")

Conversion Functions

cograph provides functions to convert between formats. All conversion functions accept any supported input type.

as_cograph() / to_cograph() — Import to cograph

Convert any format to a cograph_network object.

Accepted inputs:

matrix — Adjacency/weight matrix
data.frame — Edge list with from/to/weight columns
igraph — igraph object
network — statnet network object
qgraph — qgraph object
tna — TNA model object
group_tna — Grouped TNA model object

# From matrix
net <- as_cograph(adj_matrix)
print(net)
#> Cograph network: 4 nodes, 5 edges ( undirected )
#> Source: matrix 
#>   Nodes (4): A, B, C, D
#>   Edges: 5 / 6 (density: 83.3%)
#>   Weights: [0.200, 0.800]  |  mean: 0.560
#>   Strongest edges:
#>     A -- B  0.800
#>     C -- D  0.700
#>     B -- C  0.600
#>     A -- C  0.500
#>     A -- D  0.200
#> Layout: none

# From edge list
net_edges <- as_cograph(edges)
print(net_edges)
#> Cograph network: 4 nodes, 6 edges ( undirected )
#> Source: edgelist 
#> Data: data.frame (6 x 3) 
#>   Nodes (4): Alice, Bob, Carol, Dave
#> Weights: 0.3 to 0.9 
#> Layout: none

# Override auto-detected directedness
net_directed <- as_cograph(adj_matrix, directed = TRUE)
cat("Directed:", attr(net_directed, "directed"), "\n")
#> Directed:

# From igraph
g <- igraph::make_ring(5)
igraph::V(g)$name <- LETTERS[1:5]
net_from_igraph <- as_cograph(g)
print(net_from_igraph)
#> Cograph network: 5 nodes, 5 edges ( undirected )
#> Source: igraph 
#>   Nodes (5): A, B, C, D, E
#> Weights: 1 (all equal)
#> Layout: none

The function is idempotent — passing a cograph_network returns it unchanged.

to_igraph() — Export to igraph

Convert any format to an igraph object:

# From matrix
g <- to_igraph(adj_matrix)
cat("Vertices:", igraph::vcount(g), "\n")
#> Vertices: 4
cat("Edges:", igraph::ecount(g), "\n")
#> Edges: 5

# From cograph_network
net <- as_cograph(adj_matrix)
g2 <- to_igraph(net)

# Check attributes preserved
cat("Vertex names:", paste(igraph::V(g2)$name, collapse = ", "), "\n")
#> Vertex names: A, B, C, D

to_data_frame() / to_df() — Export to Edge List

Convert any format to an edge list data frame:

# From matrix
df <- to_df(adj_matrix)
print(df)
#>   from to weight
#> 1    A  B    0.8
#> 2    A  C    0.5
#> 3    A  D    0.2
#> 4    B  C    0.6
#> 5    C  D    0.7

# From cograph_network
net <- as_cograph(adj_matrix)
df2 <- to_df(net)
print(df2)
#>   from to weight
#> 1    A  B    0.8
#> 2    A  C    0.5
#> 3    B  C    0.6
#> 4    A  D    0.2
#> 5    C  D    0.7

Output columns: from, to, weight

to_matrix() — Export to Adjacency Matrix

Convert any format to an adjacency matrix:

# From cograph_network
net <- as_cograph(edges)
mat <- to_matrix(net)
print(mat)
#>       Alice Bob Carol Dave
#> Alice   0.0 0.9   0.5  0.4
#> Bob     0.9 0.0   0.7  0.3
#> Carol   0.5 0.7   0.0  0.8
#> Dave    0.4 0.3   0.8  0.0

# From igraph (with weights)
g <- igraph::graph_from_adjacency_matrix(
  matrix(c(0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0), 4, 4,
         dimnames = list(c("W", "X", "Y", "Z"), c("W", "X", "Y", "Z"))),
  mode = "undirected",
  weighted = TRUE
)
mat <- to_matrix(g)
print(mat)
#>   W X Y Z
#> W 0 1 0 1
#> X 1 0 1 0
#> Y 0 1 0 1
#> Z 1 0 1 0

Returns a square numeric matrix with row/column names preserved.

to_network() — Export to statnet network

Convert any format to a statnet network object:

# Convert matrix to statnet network
statnet_net <- to_network(adj_matrix)
print(statnet_net)
#>  Network attributes:
#>   vertices = 4 
#>   directed = FALSE 
#>   hyper = FALSE 
#>   loops = FALSE 
#>   multiple = FALSE 
#>   bipartite = FALSE 
#>   total edges= 5 
#>     missing edges= 0 
#>     non-missing edges= 5 
#> 
#>  Vertex attribute names: 
#>     vertex.names 
#> 
#>  Edge attribute names: 
#>     weight

Requires the network package. Preserves edge weights and vertex names.

Conversion Summary

Function	Output	Use Case
`as_cograph()` / `to_cograph()`	`cograph_network`	Import for visualization with splot
`to_igraph()`	`igraph`	Use igraph’s analysis functions
`to_df()`	`data.frame`	Export to CSV, database, or other tools
`to_matrix()`	`matrix`	Export to adjacency matrix for other packages
`to_network()`	`network`	Use with statnet/ergm ecosystem

Format Summary

Input	Class	Directed	Weights	Labels
Matrix	`matrix`	Symmetry check	Cell values	dimnames
Edge list	`data.frame`	Reciprocal check	weight column	Unique nodes
igraph	`igraph`	`is_directed()`	weight attr	name attr
network	`network`	`is.directed()`	weight attr	vertex.names
qgraph	`qgraph`	Edgelist	Edgelist	Node names
tna	`tna`	Always TRUE	`$weights`	`$labels`