Density-based Community Detection/Optimization

TL;DR

This paper explores a density optimization approach for community detection that improves community density metrics without significantly affecting modularity, and introduces a new algorithm based on strongly connected components.

Contribution

It proposes a density optimization method for label propagation communities and introduces a new community detection algorithm using strongly connected components.

Findings

Optimization improves community density metrics.

Community detection results are comparable to benchmark algorithms.

Density optimization does not significantly alter modularity.

Abstract

Modularity-based algorithms used for community detection have been increasing in recent years. Modularity and its application have been generating controversy since some authors argue it is not a metric without disadvantages. It has been shown that algorithms that use modularity to detect communities suffer a resolution limit and, therefore, it is unable to identify small communities in some situations. In this work, we try to apply a density optimization of communities found by the label propagation algorithm and study what happens regarding modularity of optimized results. We introduce a metric we call ADC (Average Density per Community); we use this metric to prove our optimization provides improvements to the community density obtained with benchmark algorithms. Additionally, we provide evidence this optimization might not alter modularity of resulting communities significantly. Additionally, by also using the SSC (Strongly Connected Components) concept we developed a community detection algorithm that we also compare with the label propagation algorithm. These comparisons were executed with several test networks and with different network sizes. The results of the optimization algorithm proved to be interesting. Additionally, the results of the community detection algorithm turned out to be similar to the benchmark algorithm we used.