# walktrap.community

0th

Percentile

##### Community strucure via short random walks

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.

Keywords
graphs
##### 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.
##### 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

##### Value

• A named list with three members:
• mergesThe 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.
• modularityNumeric vector, the modularity score of the current community structure after each merge operation.
• labelsThe labels of the vertices in the graph. The name vertex attribute will be copied here, if exists.

##### concept

• Random walk
• Community structure

##### References

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

modularity and fastgreedy.community, spinglass.community, leading.eigenvector.community, edge.betweenness.community for other community detection methods.

##### Aliases
• walktrap.community
##### Examples
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)
Documentation reproduced from package igraph, version 0.5.3, License: GPL (>= 2)

### Community examples

Looks like there are no examples yet.