infomap.community

0th

Percentile

Infomap community finding

Find community structure that minimizes the expected description length of a random walker trajectory

Keywords
graphs
Usage
infomap.community (graph, e.weights = NULL, v.weights = NULL,
                   nb.trials = 10, modularity = TRUE)
Arguments
graph
The input graph.
e.weights
If not NULL, then a numeric vector of edge weights. The length must match the number of edges in the graph. By default the weight edge attribute is used as weights. If it is not present, then all edge
v.weights
If not NULL, then a numeric vector of vertex weights. The length must match the number of vertices in the graph. By default the weight vertex attribute is used as weights. If it is not present, then a
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.
Details

Please see the details of this method in the references given below.

Value

concept

Community structure

References

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.

See Also

Other community finding methods and communities.

Aliases
  • infomap.community
Examples
## Zachary's karate club
g <- graph.famous("Zachary")

imc <- infomap.community(g)
membership(imc)
communities(imc)
Documentation reproduced from package igraph, version 0.6.5-2, License: GPL (>= 2)

Community examples

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