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,
...
)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)
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
Examples
# Basic usage with adjacency matrix
adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
network_summary(adj)
#> node_count edge_count density component_count diameter mean_distance min_cut
#> 1 3 3 1 1 1 1 2
#> centralization_degree centralization_in_degree centralization_out_degree
#> 1 0 NA NA
#> centralization_betweenness centralization_closeness centralization_eigen
#> 1 0 0 0
#> transitivity reciprocity assortativity_degree hub_score authority_score
#> 1 1 NA NaN NA NA
# With detailed statistics
network_summary(adj, detailed = TRUE)
#> node_count edge_count density component_count diameter mean_distance min_cut
#> 1 3 3 1 1 1 1 2
#> centralization_degree centralization_in_degree centralization_out_degree
#> 1 0 NA NA
#> centralization_betweenness centralization_closeness centralization_eigen
#> 1 0 0 0
#> transitivity reciprocity assortativity_degree hub_score authority_score
#> 1 1 NA NaN NA NA
#> mean_degree sd_degree median_degree mean_strength sd_strength
#> 1 2 0 2 2 0
#> mean_betweenness mean_closeness mean_eigenvector mean_pagerank
#> 1 0 0.5 1 0.333
#> mean_constraint mean_local_transitivity
#> 1 1.125 1
# With extended structural metrics
network_summary(adj, extended = TRUE)
#> node_count edge_count density component_count diameter mean_distance min_cut
#> 1 3 3 1 1 1 1 2
#> centralization_degree centralization_in_degree centralization_out_degree
#> 1 0 NA NA
#> centralization_betweenness centralization_closeness centralization_eigen
#> 1 0 0 0
#> transitivity reciprocity assortativity_degree hub_score authority_score girth
#> 1 1 NA NaN NA NA 3
#> radius vertex_connectivity largest_clique_size cut_vertex_count bridge_count
#> 1 1 2 3 0 0
#> global_efficiency local_efficiency
#> 1 1 1
# All metrics
network_summary(adj, detailed = TRUE, extended = TRUE)
#> node_count edge_count density component_count diameter mean_distance min_cut
#> 1 3 3 1 1 1 1 2
#> centralization_degree centralization_in_degree centralization_out_degree
#> 1 0 NA NA
#> centralization_betweenness centralization_closeness centralization_eigen
#> 1 0 0 0
#> transitivity reciprocity assortativity_degree hub_score authority_score girth
#> 1 1 NA NaN NA NA 3
#> radius vertex_connectivity largest_clique_size cut_vertex_count bridge_count
#> 1 1 2 3 0 0
#> global_efficiency local_efficiency mean_degree sd_degree median_degree
#> 1 1 1 2 0 2
#> mean_strength sd_strength mean_betweenness mean_closeness mean_eigenvector
#> 1 2 0 0 0.5 1
#> mean_pagerank mean_constraint mean_local_transitivity
#> 1 0.333 1.125 1
# From igraph object
if (requireNamespace("igraph", quietly = TRUE)) {
g <- igraph::erdos.renyi.game(20, 0.3)
network_summary(g)
}
#> node_count edge_count density component_count diameter mean_distance min_cut
#> 1 20 52 0.274 2 3 1.801 0
#> centralization_degree centralization_in_degree centralization_out_degree
#> 1 0.147 NA NA
#> centralization_betweenness centralization_closeness centralization_eigen
#> 1 0.076 NaN 0.36
#> transitivity reciprocity assortativity_degree hub_score authority_score
#> 1 0.297 NA -0.257 NA NA