cluster_walktrap

0th

Percentile

Community strucure via short random walks

This function tries to find densely connected subgraphs, also called communities in a graph via random walks. The idea is that short random walks tend to stay in the same community.

Keywords
graphs
Usage
cluster_walktrap(graph, weights = E(graph)$weight, steps = 4, merges = TRUE, modularity = TRUE, membership = TRUE)
Arguments
graph
The input graph, edge directions are ignored in directed graphs.
weights
The edge weights.
steps
The length of the random walks to perform.
merges
Logical scalar, whether to include the merge matrix in the result.
modularity
Logical scalar, whether to include the vector of the modularity scores in the result. If the membership argument is true, then it will be always calculated.
membership
Logical scalar, whether to calculate the membership vector for the split corresponding to the highest modularity value.
Details

This function is the implementation of the Walktrap community finding algorithm, see Pascal Pons, Matthieu Latapy: Computing communities in large networks using random walks, http://arxiv.org/abs/physics/0512106

Value

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

References

Pascal Pons, Matthieu Latapy: Computing communities in large networks using random walks, http://arxiv.org/abs/physics/0512106

See Also

See communities on getting the actual membership vector, merge matrix, modularity score, etc.

modularity and cluster_fast_greedy, cluster_spinglass, cluster_leading_eigen, cluster_edge_betweenness for other community detection methods.

Aliases
  • cluster_walktrap
  • walktrap.community
Examples
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_walktrap(g)
Documentation reproduced from package igraph, version 1.0.1, License: GPL (>= 2)

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