Cluster Quality Metrics — cluster

Computes per-cluster and global quality metrics for network partitioning. Supports both binary and weighted networks.

Usage

cluster_quality(x, clusters, weighted = TRUE, directed = TRUE)

cqual(x, clusters, weighted = TRUE, directed = TRUE)

Arguments

x: Adjacency matrix (numeric)
clusters: Cluster specification (named list, data frame, or membership vector; see csum)
weighted: Logical; if TRUE (default), use edge weights; if FALSE, binarize the matrix first
directed: Logical; if TRUE (default), treat as directed network

Value

A cluster_quality object (a list) with:

per_cluster: Data frame, one row per cluster, with columns cluster (index), cluster_name, n_nodes, internal_edges (within-cluster weight), cut_edges (boundary-crossing weight), internal_density, avg_internal_degree, expansion, cut_ratio and conductance.
global: List with modularity (Newman-Girvan, computed on the weighted or binarized matrix), coverage (share of total weight that is internal to some cluster) and n_clusters.

See cluster_quality.

Examples

clusters <- list(C1 = c("Explore", "Reflect", "Discuss"),
                 C2 = c("Plan", "Create", "Share"),
                 C3 = c("Monitor", "Adapt", "Synthesize", "Evaluate"))
cluster_quality(regulation_net, clusters = clusters)
#> Cluster Quality Metrics
#> =======================
#> 
#> Global metrics:
#>   Modularity: 0.081 
#>   Coverage:   0.4033 
#>   Clusters:   3 
#> 
#> Per-cluster metrics:
#>  cluster cluster_name n_nodes internal_edges cut_edges internal_density
#>        1           C1       3           1.05      2.21       0.17500000
#>        2           C2       3           1.00      3.49       0.16666667
#>        3           C3       4           1.16      3.80       0.09666667
#>  avg_internal_degree expansion cut_ratio conductance
#>            0.7000000 0.7366667 0.1052381   0.5127610
#>            0.6666667 1.1633333 0.1661905   0.6357013
#>            0.5800000 0.9500000 0.1583333   0.6209150