igraph (version 0.5.1)

# walktrap.community: 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

```walktrap.community(graph, weights = E(graph)\$weight, steps = 4, merges =
TRUE, modularity = FALSE, labels = TRUE)```

## Arguments

graph
The input graph.
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.
labels
Logical scalar, if `TRUE` and the graph has a vertex attribute called `name` then it will be included in the result, in the list member called `labels`.

## Value

• A named list with three members:
• merges
{ The merges performed by the algorithm will be stored here. Each merge is a row in a two-column matrix and contains the ids of the merged communities. Communities are numbered from zero and cluster number smaller than the number of nodes in the network belong to the individual vertices as singleton communities. In each step a new community is created from two other communities and its id will be one larger than the largest community id so far. This means that before the first merge we have n communities (the number of vertices in the graph) numbered from zero to n-1. The first merge created community n, the second community n+1, etc. modularity{ Numeric vector, the modularity score of the current community structure after each merge operation. } labels{The labels of the vertices in the graph. The `name` vertex attribute will be copied here, if exists. } Pascal Pons, Matthieu Latapy: Computing communities in large networks using random walks, http://arxiv.org/abs/physics/0512106 [object Object],[object Object],[object Object] `modularity` and `fastgreedy.community`, `spinglass.community`, `leading.eigenvector.community`, `edge.betweenness.community` for other community detection methods. g <- graph.full(5) %du% graph.full(5) %du% graph.full(5) g <- add.edges(g, c(0,5, 0,10, 5, 10)) walktrap.community(g) graphs

## concept

• Random walk
• Community structure

## 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