This is useful if you want to use functions defined on membership vectors, but your membership vector does not come from an igraph clustering method.
See also
Community detection
cluster_edge_betweenness()
,
cluster_fast_greedy()
,
cluster_fluid_communities()
,
cluster_infomap()
,
cluster_label_prop()
,
cluster_leading_eigen()
,
cluster_leiden()
,
cluster_louvain()
,
cluster_optimal()
,
cluster_spinglass()
,
cluster_walktrap()
,
compare()
,
groups()
,
make_clusters()
,
modularity.igraph()
,
plot_dendrogram()
,
split_join_distance()
Examples
## Compare to the correct clustering
g <- (make_full_graph(10) + make_full_graph(10)) %>%
rewire(each_edge(p = 0.2))
correct <- rep(1:2, each = 10) %>% as_membership()
fc <- cluster_fast_greedy(g)
compare(correct, fc)
#> [1] 0
compare(correct, membership(fc))
#> [1] 0