cluster_fluid_communities | R Documentation |
The algorithm detects communities based on the simple idea of several fluids interacting in a non-homogeneous environment (the graph topology), expanding and contracting based on their interaction and density.
cluster_fluid_communities(graph, no.of.communities)
graph |
The input graph. The graph must be simple and connected. Empty graphs are not supported as well as single vertex graphs. Edge directions are ignored. Weights are not considered. |
no.of.communities |
The number of communities to be found. Must be greater than 0 and fewer than number of vertices in the graph. |
cluster_fluid_communities()
returns a communities()
object, please see the communities()
manual page for details.
Ferran Parés
Parés F, Gasulla DG, et. al. (2018) Fluid Communities: A Competitive, Scalable and Diverse Community Detection Algorithm. In: Complex Networks & Their Applications VI: Proceedings of Complex Networks 2017 (The Sixth International Conference on Complex Networks and Their Applications), Springer, vol 689, p 229, doi: 10.1007/978-3-319-72150-7_19
See communities()
for extracting the membership,
modularity scores, etc. from the results.
Other community detection algorithms: cluster_walktrap()
,
cluster_spinglass()
,
cluster_leading_eigen()
,
cluster_edge_betweenness()
,
cluster_fast_greedy()
,
cluster_label_prop()
cluster_louvain()
,
cluster_leiden()
Community detection
as_membership()
,
cluster_edge_betweenness()
,
cluster_fast_greedy()
,
cluster_infomap()
,
cluster_label_prop()
,
cluster_leading_eigen()
,
cluster_leiden()
,
cluster_louvain()
,
cluster_optimal()
,
cluster_spinglass()
,
cluster_walktrap()
,
compare()
,
groups()
,
make_clusters()
,
membership()
,
modularity.igraph()
,
plot_dendrogram()
,
split_join_distance()
,
voronoi_cells()
g <- make_graph("Zachary")
comms <- cluster_fluid_communities(g, 2)
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