Finding community structure by multi-level optimization of modularity
This function implements the multi-level modularity optimization algorithm for finding community structure, see references below. It is based on the modularity measure and a hierarchical approach.
cluster_louvain(graph, weights = NULL)
graph |
The input graph. |
weights |
Optional positive weight vector. If the graph has a
|
This function implements the multi-level modularity optimization algorithm for finding community structure, see VD Blondel, J-L Guillaume, R Lambiotte and E Lefebvre: Fast unfolding of community hierarchies in large networks, https://arxiv.org/abs/0803.0476 for the details.
It is based on the modularity measure and a hierarchical approach. Initially, each vertex is assigned to a community on its own. In every step, vertices are re-assigned to communities in a local, greedy way: each vertex is moved to the community with which it achieves the highest contribution to modularity. When no vertices can be reassigned, each community is considered a vertex on its own, and the process starts again with the merged communities. The process stops when there is only a single vertex left or when the modularity cannot be increased any more in a step.
This function was contributed by Tom Gregorovic.
cluster_louvain returns a communities
object, please see the communities manual page for details.
Tom Gregorovic, Tamas Nepusz ntamas@gmail.com
Vincent D. Blondel, Jean-Loup Guillaume, Renaud Lambiotte, Etienne Lefebvre: Fast unfolding of communities in large networks. J. Stat. Mech. (2008) P10008
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_leiden
# This is so simple that we will have only one level g <- make_full_graph(5) %du% make_full_graph(5) %du% make_full_graph(5) g <- add_edges(g, c(1,6, 1,11, 6, 11)) cluster_louvain(g)
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