Community detection algorithm based on interacting fluids
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
g <- graph.famous("Zachary")
comms <- cluster_fluid_communities(g, 2)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.