Use this if you are using igraph from R
This is a fast, nearly linear time algorithm for detecting community structure in networks. In works by labeling the vertices with unique labels and then updating the labels by majority voting in the neighborhood of the vertex.
cluster_label_prop(graph, weights = NULL, initial = NULL, fixed = NULL)
graph |
The input graph, should be undirected to make sense. |
weights |
An optional weight vector. It should contain a positive
weight for all the edges. The ‘weight’ edge attribute is used if
present. Supply ‘ |
initial |
The initial state. If |
fixed |
Logical vector denoting which labels are fixed. Of course this makes sense only if you provided an initial state, otherwise this element will be ignored. Also note that vertices without labels cannot be fixed. |
This function implements the community detection method described in: Raghavan, U.N. and Albert, R. and Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E 76, 036106. (2007). This version extends the original method by the ability to take edge weights into consideration and also by allowing some labels to be fixed.
From the abstract of the paper: “In our algorithm every node is initialized with a unique label and at every step each node adopts the label that most of its neighbors currently have. In this iterative process densely connected groups of nodes form a consensus on a unique label to form communities.”
cluster_label_prop
returns a
communities
object, please see the communities
manual page for details.
Tamas Nepusz ntamas@gmail.com for the C implementation, Gabor Csardi csardi.gabor@gmail.com for this manual page.
Raghavan, U.N. and Albert, R. and Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E 76, 036106. (2007)
communities
for extracting the actual results.
cluster_fast_greedy
, cluster_walktrap
,
cluster_spinglass
, cluster_louvain
and
cluster_leiden
for other community detection methods.
g <- sample_gnp(10, 5/10) %du% sample_gnp(9, 5/9) g <- add_edges(g, c(1, 12)) cluster_label_prop(g)