Finding overlapping communities in networks by label propagation

TL;DR

This paper introduces an efficient label propagation algorithm capable of detecting overlapping communities in large, weighted, and bipartite networks, outperforming existing methods in speed and accuracy.

Contribution

The main novelty is extending label propagation to allow vertices to belong to multiple communities simultaneously, enabling detection of overlapping structures.

Findings

Effective in recovering overlapping communities in large networks

Handles weighted and bipartite networks

Processes large, dense networks quickly

Abstract

We propose an algorithm for finding overlapping community structure in very large networks. The algorithm is based on the label propagation technique of Raghavan, Albert, and Kumara, but is able to detect communities that overlap. Like the original algorithm, vertices have labels that propagate between neighbouring vertices so that members of a community reach a consensus on their community membership. Our main contribution is to extend the label and propagation step to include information about more than one community: each vertex can now belong to up to v communities, where v is the parameter of the algorithm. Our algorithm can also handle weighted and bipartite networks. Tests on an independently designed set of benchmarks, and on real networks, show the algorithm to be highly effective in recovering overlapping communities. It is also very fast and can process very large and dense networks in a short time.