igraph (version 1.3.5)

cluster_label_prop: Finding communities based on propagating labels

Description

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.

Usage

cluster_label_prop(graph, weights = NULL, initial = NULL, fixed = NULL)

Value

cluster_label_prop returns a communities object, please see the communities

manual page for details.

Arguments

graph

The input graph, should be undirected to make sense.

weights

The weights of the edges. It must be a positive numeric vector, NULL or NA. If it is NULL and the input graph has a ‘weight’ edge attribute, then that attribute will be used. If NULL and no such attribute is present, then the edges will have equal weights. Set this to NA if the graph was a ‘weight’ edge attribute, but you don't want to use it for community detection. A larger edge weight means a stronger connection for this function.

initial

The initial state. If NULL, every vertex will have a different label at the beginning. Otherwise it must be a vector with an entry for each vertex. Non-negative values denote different labels, negative entries denote vertices without labels.

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.

Author

Tamas Nepusz for the C implementation, Gabor Csardi for this manual page.

Details

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.”

References

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)

See Also

communities for extracting the actual results.

cluster_fast_greedy, cluster_walktrap, cluster_spinglass, cluster_louvain and cluster_leiden for other community detection methods.

Examples

  g <- sample_gnp(10, 5/10) %du% sample_gnp(9, 5/9)
  g <- add_edges(g, c(1, 12))
  cluster_label_prop(g)