R igraph manual pages

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Finding community structure by multi-level optimization of modularity

Description

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 hierarchial approach.

Usage

cluster_louvain(graph, weights = NULL)


Arguments

 graph The input graph. weights Optional positive weight vector. If the graph has a weight edge attribute, then this is used by default. Supply NA here if the graph has a weight edge attribute, but you want to ignore it. Larger edge weights correspond to stronger connections.

Details

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, http://arxiv.org/abs/arXiv:0803.0476 for the details.

It is based on the modularity measure and a hierarchial 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.

Value

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

Author(s)

Tom Gregorovic, Tamas Nepusz ntamas@gmail.com

References

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

Examples


# 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)



[Package igraph version 1.2.5 Index]