Obtaining Communities with a Fitness Growth Process

TL;DR

This paper introduces a novel local growth process for community detection in networks, dynamically adjusting scale parameters to improve partition quality, and evaluates its effectiveness on benchmark graphs.

Contribution

It proposes a new local community detection method based on a fitness growth process that covers entire networks and adapts scale parameters dynamically.

Findings

The method effectively detects communities in heterogeneous graphs.

Local techniques outperform global methods in benchmark tests.

Dynamic scale adjustment improves community detection accuracy.

Abstract

The study of community structure has been a hot topic of research over the last years. But, while successfully applied in several areas, the concept lacks of a general and precise notion. Facts like the hierarchical structure and heterogeneity of complex networks make it difficult to unify the idea of community and its evaluation. The global functional known as modularity is probably the most used technique in this area. Nevertheless, its limits have been deeply studied. Local techniques as the ones by Lancichinetti et al. and Palla et al. arose as an answer to the resolution limit and degeneracies that modularity has. Here we start from the algorithm by Lancichinetti et al. and propose a unique growth process for a fitness function that, while being local, finds a community partition that covers the whole network, updating the scale parameter dynamically. We test the quality of our results by using a set of benchmarks of heterogeneous graphs. We discuss alternative measures for evaluating the community structure and, in the light of them, infer possible explanations for the better performance of local methods compared to global ones in these cases.