cograph (version 2.0.0)

summarize_network: Summarize Network by Clusters

Description

Creates a summary network where each cluster becomes a single node. Edge weights are aggregated from the original network using the specified method. Returns a cograph_network object ready for plotting.

Usage

summarize_network(
  x,
  cluster_list = NULL,
  method = c("sum", "mean", "max", "min", "median", "density", "geomean"),
  directed = TRUE
)
cluster_network(
  x,
  cluster_list = NULL,
  method = c("sum", "mean", "max", "min", "median", "density", "geomean"),
  directed = TRUE
)
cnet(
  x,
  cluster_list = NULL,
  method = c("sum", "mean", "max", "min", "median", "density", "geomean"),
  directed = TRUE
)

Value

A cograph_network object with:

• One node per cluster (named by cluster)

• Edge weights = aggregated between-cluster weights

• nodes$size = cluster sizes (number of original nodes)

See summarize_network.

See summarize_network.

Arguments

x

A weight matrix, tna object, or cograph_network.

cluster_list

Cluster specification:

• Named list of node vectors (e.g., list(A = c("n1", "n2"), B = c("n3", "n4")))

• String column name from nodes data (e.g., "clusters", "groups")

• NULL to auto-detect from common column names

method

Aggregation method for edge weights: "sum", "mean", "max", "min", "median", "density", "geomean". Default "sum".

directed

Logical. Treat network as directed. Default TRUE.

See Also

cluster_summary, plot_mcml

Examples

# Create a network with clusters
mat <- matrix(runif(100), 10, 10)
diag(mat) <- 0
rownames(mat) <- colnames(mat) <- LETTERS[1:10]

# Define clusters
clusters <- list(
  Group1 = c("A", "B", "C"),
  Group2 = c("D", "E", "F"),
  Group3 = c("G", "H", "I", "J")
)

# Create summary network
summary_net <- summarize_network(mat, clusters)
splot(summary_net)

# With cograph_network (auto-detect clusters column)
Net <- cograph(mat)
Net$nodes$clusters <- rep(c("A", "B", "C"), c(3, 3, 4))
summary_net <- summarize_network(Net)  # Auto-detects 'clusters'