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 hierarchial approach.
multilevel.community (graph, weights = NULL)
- The input graph.
- Optional positive weight vector.
If the graph has a
weightedge attribute, then this is used by default. Supply
NAhere if the graph has a
weightedge attribute, but you want to ignore it.
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,
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.
- Community structure
- Multilevel community detection
Vincent D. Blondel, Jean-Loup Guillaume, Renaud Lambiotte, Etienne Lefebvre: Fast unfolding of communities in large networks. J. Stat. Mech. (2008) P10008
communities for extracting the membership,
modularity scores, etc. from the results.
Other community detection algorithms:
# This is so simple that we will have only one level g <- graph.full(5) %du% graph.full(5) %du% graph.full(5) g <- add.edges(g, c(1,6, 1,11, 6, 11)) multilevel.community(g)