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)Value
A cluster_quality object with:
- per_cluster
Data frame with per-cluster metrics
- global
List of global metrics (modularity, coverage)
See cluster_quality.
Examples
mat <- matrix(runif(100), 10, 10)
diag(mat) <- 0
clusters <- c(1,1,1,2,2,2,3,3,3,3)
q <- cluster_quality(mat, clusters)
q$per_cluster # Per-cluster metrics
#> cluster cluster_name n_nodes internal_edges cut_edges internal_density
#> 1 1 1 3 2.043959 21.44976 0.3406598
#> 2 2 2 3 3.016079 23.13639 0.5026799
#> 3 3 3 4 9.159230 23.36489 0.7632691
#> avg_internal_degree expansion cut_ratio conductance
#> 1 1.362639 7.149920 1.0214171 0.8399260
#> 2 2.010720 7.712130 1.1017328 0.7931965
#> 3 4.579615 5.841223 0.9735371 0.5605329
q$global # Modularity, coverage
#> $modularity
#> [1] -0.05272447
#>
#> $coverage
#> [1] 0.2950375
#>
#> $n_clusters
#> [1] 3
#>
mat <- matrix(runif(100), 10, 10)
diag(mat) <- 0
cqual(mat, c(1,1,1,2,2,2,3,3,3,3))
#> Cluster Quality Metrics
#> =======================
#>
#> Global metrics:
#> Modularity: -0.0673
#> Coverage: 0.271
#> Clusters: 3
#>
#> Per-cluster metrics:
#> cluster cluster_name n_nodes internal_edges cut_edges internal_density
#> 1 1 3 4.392079 21.96938 0.7320132
#> 2 2 3 2.757767 22.01327 0.4596278
#> 3 3 4 5.831567 25.85041 0.4859639
#> avg_internal_degree expansion cut_ratio conductance
#> 2.928053 7.323126 1.046161 0.7143691
#> 1.838511 7.337758 1.048251 0.7996450
#> 2.915783 6.462602 1.077100 0.6890954