walktrap.community {igraph} | R Documentation |
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.
walktrap.community(graph, weights = E(graph)$weight, steps = 4, merges = TRUE, modularity = TRUE, labels = TRUE, membership = TRUE)
graph |
The input graph, edge directions are ignored in directed graphs. |
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. If the |
labels |
Logical scalar, if |
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
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 |
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
|
membership |
If requested, then the membership vector that belongs to the best split, in terms of highest modularity score. |
Pascal Pons google@for.it and Gabor Csardi csardi@rmki.kfki.hu for the R and igraph interface
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.
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)