A Flexible Fitness Function for Community Detection in Complex Networks
This addresses the issue of inaccurate community partitions in complex networks for researchers and practitioners, but it is incremental as it builds on existing optimization methods with a new fitness function.
The paper tackled the problem of community detection in complex networks by proposing a new flexible fitness function that allows identification of communities with distinct characteristics, and results showed that partitions obtained were much closer to ground-truth than those from modularity optimization.
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.