A Flexible Fitness Function for Community Detection in Complex Networks

TL;DR

This paper introduces a new adaptable fitness function for community detection in complex networks, enabling identification of diverse community types and outperforming traditional modularity-based methods in benchmark tests.

Contribution

It proposes a flexible fitness function for community detection that captures various community characteristics, tested with an immune-inspired optimization algorithm.

Findings

Partitions closer to ground-truth than modularity optimization

Effective in detecting both disjoint and overlapping communities

Demonstrated improved accuracy on benchmark networks

Abstract

Most community detection algorithms from the literature work as optimization tools that minimize a given \textit{fitness function}, while assuming that each node belongs to a single community. Since there is no hard concept of what a community is, most proposed fitness functions focus on a particular definition. As such, these functions do not always lead to partitions that correspond to those observed in practice. This paper proposes a new flexible fitness function that allows the identification of communities with distinct characteristics. Such flexibility was evaluated through the adoption of an immune-inspired optimization algorithm, named cob-aiNet[C], to identify both disjoint and overlapping communities in a set of benchmark networks. The results have shown that the obtained partitions are much closer to the ground-truth than those obtained by the optimization of the modularity function.