Computes network-level statistics and returns them as a one-row data frame. The basic set covers size, density, connectivity, path lengths, centralization, transitivity, reciprocity and degree assortativity.
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, shortest-path, and centrality calculations where the underlying igraph routine accepts them. Default TRUE.
- mode
For directed networks: "all", "in", or "out". Used by the degree, strength and closeness statistics added by
detailed = TRUE. 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, add 11 summary statistics of node-level centralities to the 16 basic metrics. Default FALSE.
- extended
Logical. If TRUE, add 8 structural metrics (girth, radius, vertex connectivity, clique size, cut vertices, bridges, global and local efficiency). Default FALSE.
- digits
Integer. Round numeric results to this many decimal places. Default 3. NULL skips rounding.
- ...
Additional arguments (currently unused)
Value
A data frame with one row. The basic measures are 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 number of edges whose removal disconnects the network. Edge weights are not used.
- centralization_degree
Degree centralization over all ties (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
The extended measures are added when extended = TRUE:
- girth
Length of shortest cycle (Inf if acyclic)
- radius
Minimum eccentricity over all nodes
- 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
The detailed measures are added 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
network_summary(regulation_net)
#> node_count edge_count density component_count diameter mean_distance min_cut
#> 1 10 30 0.333 1 0.97 0.435 1
#> centralization_degree centralization_in_degree centralization_out_degree
#> 1 0.123 0.333 0.222
#> centralization_betweenness centralization_closeness centralization_eigen
#> 1 0.149 0.238 0.479
#> transitivity reciprocity assortativity_degree
#> 1 0.423 0.111 -0.116