R: Community structure via short random walks

cluster_walktrap {igraph} | R Documentation

## Community structure 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 = NULL,
      steps = 4,
      merges = TRUE,
      modularity = TRUE,
      membership = TRUE
    )

### Arguments

`graph` | The input graph, edge directions are ignored in directed graphs.
`weights` | The weights of the edges. It must be a positive numeric vector, `NULL` or `NA`. If it is `NULL` and the input graph has a ‘weight’ edge attribute, then that attribute will be used. If `NULL` and no such attribute is present, then the edges will have equal weights. Set this to `NA` if the graph was a ‘weight’ edge attribute, but you don't want to use it for community detection. 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 always be 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 1.3.1 Index]