igraph (version 1.2.7)

cluster_fluid_communities: Community detection algorithm based on interacting fluids

Description

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.

Usage

cluster_fluid_communities(graph, no.of.communities)

Arguments

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.

Value

cluster_fluid_communities returns a communities object, please see the communities manual page for details.

References

Par<U+00E9>s F, Gasulla DG, et. al. (2018) Fluid Communities: A Competitive, Scalable and Diverse Community Detection Algorithm. In: Complex Networks &amp; 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 Also

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

Examples

# NOT RUN {
g <- graph.famous("Zachary")
comms <- cluster_fluid_communities(g, 2)
# }