Input Formats

In cograph: Analysis and Visualization of Complex Networks

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 6,
  fig.height = 5,
  fig.dpi = 50,
  dpi = 50
)
library(cograph)

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)

# 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)

# From edge list
net_edges <- as_cograph(edges)
print(net_edges)

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

# From igraph
g <- igraph::make_ring(5)
igraph::V(g)$name <- LETTERS[1:5]
net_from_igraph <- as_cograph(g)
print(net_from_igraph)

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")
cat("Edges:", igraph::ecount(g), "\n")

# 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")

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 cograph_network
net <- as_cograph(adj_matrix)
df2 <- to_df(net)
print(df2)

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)

# 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)

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)

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 |

cograph documentation built on April 1, 2026, 1:07 a.m.