cluster_walktrap: Community strucure via short random walks

Description

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.

The input graph, edge directions are ignored in directed
graphs.

weights

The edge weights. Larger edge weights increase the
probability that an edge is selected by the random walker. In other
words, larger edge weights correspond to stronger connections.

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.

Value

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

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

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.