cluster_infomap | R Documentation |
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 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) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1073/pnas.0706851105")}, https://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). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1140/epjst/e2010-01179-1")}, https://arxiv.org/abs/0906.1405.
Other community finding methods and communities()
.
Community detection
as_membership()
,
cluster_edge_betweenness()
,
cluster_fast_greedy()
,
cluster_fluid_communities()
,
cluster_label_prop()
,
cluster_leading_eigen()
,
cluster_leiden()
,
cluster_louvain()
,
cluster_optimal()
,
cluster_spinglass()
,
cluster_walktrap()
,
compare()
,
groups()
,
make_clusters()
,
membership()
,
modularity.igraph()
,
plot_dendrogram()
,
split_join_distance()
## Zachary's karate club
g <- make_graph("Zachary")
imc <- cluster_infomap(g)
membership(imc)
communities(imc)
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