cograph (version 2.0.0)

network_summary: Network-Level Summary Statistics

Description

Computes comprehensive network-level statistics for a network. Returns a data frame with one row containing various metrics including density, centralization scores, transitivity, and more.

Usage

network_summary(
  x,
  directed = NULL,
  weighted = TRUE,
  mode = "all",
  loops = TRUE,
  simplify = "sum",
  detailed = FALSE,
  extended = FALSE,
  digits = 3,
  ...
)

Value

A data frame with one row containing network-level statistics:

Basic measures (always computed):

node_count

Number of nodes in the network

edge_count

Number of edges in the network

density

Edge density (proportion of possible edges)

component_count

Number of connected components

diameter

Longest shortest path in the network

mean_distance

Average shortest path length

min_cut

Minimum cut value (edge connectivity)

centralization_degree

Degree centralization (0-1)

centralization_in_degree

In-degree centralization (directed only)

centralization_out_degree

Out-degree centralization (directed only)

centralization_betweenness

Betweenness centralization (0-1)

centralization_closeness

Closeness centralization (0-1)

centralization_eigen

Eigenvector centralization (0-1)

transitivity

Global clustering coefficient

reciprocity

Proportion of mutual edges (directed only)

assortativity_degree

Degree assortativity coefficient

hub_score

Maximum hub score (HITS algorithm)

authority_score

Maximum authority score (HITS algorithm)

Extended measures (when extended = TRUE):

girth

Length of shortest cycle (Inf if acyclic)

radius

Minimum eccentricity (shortest max-distance from any node)

vertex_connectivity

Minimum nodes to remove to disconnect graph

largest_clique_size

Size of the largest complete subgraph

cut_vertex_count

Number of articulation points (cut vertices)

bridge_count

Number of bridge edges

global_efficiency

Average inverse shortest path length

local_efficiency

Average local efficiency across nodes

Detailed measures (when detailed = TRUE):

mean_degree, sd_degree, median_degree

Degree distribution statistics

mean_strength, sd_strength

Weighted degree statistics

mean_betweenness

Average betweenness centrality

mean_closeness

Average closeness centrality

mean_eigenvector

Average eigenvector centrality

mean_pagerank

Average PageRank

mean_constraint

Average Burt's constraint

mean_local_transitivity

Average local clustering coefficient

Arguments

x

Network input: matrix, igraph, network, cograph_network, or tna object

directed

Logical or NULL. If NULL (default), auto-detect from matrix symmetry. Set TRUE to force directed, FALSE to force undirected.

weighted

Logical. Use edge weights for strength/centrality calculations. Default TRUE.

mode

For directed networks: "all", "in", or "out". Affects degree-based calculations. Default "all".

loops

Logical. If TRUE (default), keep self-loops. Set FALSE to remove them.

simplify

How to combine multiple edges between the same node pair. Options: "sum" (default), "mean", "max", "min", or FALSE/"none" to keep multiple edges.

detailed

Logical. If TRUE, include mean/sd centrality statistics. Default FALSE returns 18 basic metrics; TRUE returns 29 metrics.

extended

Logical. If TRUE, include additional structural metrics (girth, radius, clique size, cut vertices, bridges, efficiency). Default FALSE.

digits

Integer. Round numeric results to this many decimal places. Default 3.

...

Additional arguments (currently unused)

Examples

# Basic usage with adjacency matrix
adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
network_summary(adj)

# With detailed statistics
network_summary(adj, detailed = TRUE)

# With extended structural metrics
network_summary(adj, extended = TRUE)

# All metrics
network_summary(adj, detailed = TRUE, extended = TRUE)

# From igraph object
if (requireNamespace("igraph", quietly = TRUE)) {
  g <- igraph::erdos.renyi.game(20, 0.3)
  network_summary(g)
}