R: Community strucure via short random walks

cluster_walktrap {igraph} | R Documentation

## 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.

### 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. 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.

### 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, https://arxiv.org/abs/physics/0512106

### Value

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

### Author(s)

Pascal Pons (http://psl.pons.free.fr/) and Gabor Csardi csardi.gabor@gmail.com for the R and igraph interface

### References

Pascal Pons, Matthieu Latapy: Computing communities in large networks using random walks, https://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`, `cluster_louvain`, and `cluster_leiden` for other community detection methods.

### 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)

[Package igraph version 1.2.11 Index]