Obtaining communities with a fitness growth process

Abstract

The study of community structure became an important 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 one by Lancichinetti et al. (2009) [1] arose as an answer to the resolution limit and degeneracies that modularity has. Here we propose a unique growth process for a fitness function based on the algorithm by Lancichinetti et al. (2009) [1]. The process is local and 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 both heterogeneous and homogeneous 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.