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$per_cluster # Per-cluster metrics
#> cluster cluster_name n_nodes internal_edges cut_edges internal_density
#> 1 1 1 3 2.937610 20.93125 0.4896017
#> 2 2 2 3 3.206718 22.67670 0.5344529
#> 3 3 3 4 6.574337 25.20272 0.5478615
#> avg_internal_degree expansion cut_ratio conductance
#> 1 1.958407 6.977083 0.9967262 0.7808283
#> 2 2.137812 7.558900 1.0798429 0.7795323
#> 3 3.287169 6.300679 1.0501132 0.6571526
q$global # Modularity, coverage
#> $modularity
#> [1] -0.07088779
#>
#> $coverage
#> [1] 0.2698978
#>
#> $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: -0.0916
#> Coverage: 0.2489
#> Clusters: 3
#>
#> Per-cluster metrics:
#> cluster cluster_name n_nodes internal_edges cut_edges internal_density
#> 1 1 3 3.573873 22.84317 0.5956454
#> 2 2 3 2.872407 23.35599 0.4787344
#> 3 3 4 5.932966 28.49671 0.4944138
#> avg_internal_degree expansion cut_ratio conductance
#> 2.382582 7.614389 1.087770 0.7616696
#> 1.914938 7.785328 1.112190 0.8025892
#> 2.966483 7.124178 1.187363 0.7060170