Description Usage Arguments Details Value Author(s) References See Also Examples

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.

1 2 3 4 5 6 7 8 | ```
cluster_walktrap(
graph,
weights = E(graph)$weight,
steps = 4,
merges = TRUE,
modularity = TRUE,
membership = TRUE
)
``` |

`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` |
Logical scalar, whether to calculate the membership vector for the split corresponding to the highest modularity value. |

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

`cluster_walktrap`

returns a `communities`

object, please see the `communities`

manual page for details.

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

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

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.

1 2 3 | ```
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)
``` |

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