Use this if you are using igraph from R
Find community structure that minimizes the expected description length of a random walker trajectory
cluster_infomap(graph, e.weights = NULL, v.weights = NULL, nb.trials = 10, modularity = TRUE)
graph |
The input graph. |
e.weights |
If not |
v.weights |
If not |
nb.trials |
The number of attempts to partition the network (can be any integer value equal or larger than 1). |
modularity |
Logical scalar, whether to calculate the modularity score of the detected community structure. |
Please see the details of this method in the references given below.
cluster_infomap
returns a communities
object,
please see the communities
manual page for details.
Martin Rosvall (http://www.tp.umu.se/~rosvall/) wrote the original C++ code. This was ported to be more igraph-like by Emmanuel Navarro. The R interface and some cosmetics was done by Gabor Csardi csardi.gabor@gmail.com.
The original paper: M. Rosvall and C. T. Bergstrom, Maps of information flow reveal community structure in complex networks, PNAS 105, 1118 (2008) http://dx.doi.org/10.1073/pnas.0706851105, http://arxiv.org/abs/0707.0609
A more detailed paper: M. Rosvall, D. Axelsson, and C. T. Bergstrom, The map equation, Eur. Phys. J. Special Topics 178, 13 (2009). http://dx.doi.org/10.1140/epjst/e2010-01179-1, http://arxiv.org/abs/0906.1405.
Other community finding methods and communities
.
## Zachary's karate club g <- make_graph("Zachary") imc <- cluster_infomap(g) membership(imc) communities(imc)