cluster_walktrap  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.
cluster_walktrap(
graph,
weights = NULL,
steps = 4,
merges = TRUE,
modularity = TRUE,
membership = TRUE
)
graph 
The input graph, edge directions are ignored in directed graphs. 
weights 
The weights of the edges. It must be a positive numeric vector,

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, https://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, https://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()
, cluster_louvain()
,
and cluster_leiden()
for other community detection
methods.
Community detection
as_membership()
,
cluster_edge_betweenness()
,
cluster_fast_greedy()
,
cluster_fluid_communities()
,
cluster_infomap()
,
cluster_label_prop()
,
cluster_leading_eigen()
,
cluster_leiden()
,
cluster_louvain()
,
cluster_optimal()
,
cluster_spinglass()
,
compare()
,
groups()
,
make_clusters()
,
membership()
,
modularity.igraph()
,
plot_dendrogram()
,
split_join_distance()
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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