Seeding for pervasively overlapping communities

TL;DR

This paper investigates seed selection strategies for local fitness-based algorithms in highly overlapping networks, finding that clique-based seeds improve community detection performance.

Contribution

It demonstrates that using clique-based seeds significantly enhances the detection of overlapping communities in complex networks.

Findings

Clique seeds outperform other strategies in synthetic benchmarks.

Clique seeds improve community detection on real-world Facebook and yeast networks.

Seeding strategy is crucial in highly overlapping community detection.

Abstract

In some social and biological networks, the majority of nodes belong to multiple communities. It has recently been shown that a number of the algorithms that are designed to detect overlapping communities do not perform well in such highly overlapping settings. Here, we consider one class of these algorithms, those which optimize a local fitness measure, typically by using a greedy heuristic to expand a seed into a community. We perform synthetic benchmarks which indicate that an appropriate seeding strategy becomes increasingly important as the extent of community overlap increases. We find that distinct cliques provide the best seeds. We find further support for this seeding strategy with benchmarks on a Facebook network and the yeast interactome.