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$per_cluster # Per-cluster metrics
#> cluster cluster_name n_nodes internal_edges cut_edges internal_density
#> 1 1 1 3 3.206718 22.79075 0.5344529
#> 2 2 2 3 2.908608 23.51103 0.4847681
#> 3 3 3 4 6.730487 27.18351 0.5608740
#> avg_internal_degree expansion cut_ratio conductance
#> 1 2.137812 7.596917 1.085274 0.7803933
#> 2 1.939072 7.837010 1.119573 0.8016514
#> 3 3.365244 6.795878 1.132646 0.6688118
q$global # Modularity, coverage
#> $modularity
#> [1] -0.08264454
#>
#> $coverage
#> [1] 0.2590484
#>
#> $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: -0.085
#> Coverage: 0.2525
#> Clusters: 3
#>
#> Per-cluster metrics:
#> cluster cluster_name n_nodes internal_edges cut_edges internal_density
#> 1 1 3 2.872407 22.87844 0.4787344
#> 2 2 3 2.863043 24.22716 0.4771738
#> 3 3 4 6.405012 24.76275 0.5337510
#> avg_internal_degree expansion cut_ratio conductance
#> 1.914938 7.626147 1.089450 0.7992956
#> 1.908695 8.075719 1.153674 0.8088325
#> 3.202506 6.190687 1.031781 0.6590610