2014/06/10 by de Franca, Fabricio Olivetti, Coelho, Guilherme Palermo
#FOS: Computer and information sciences #FOS: Physical sciences #Neural and Evolutionary Computing (cs.NE) #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI)
paper · doi:10.48550/arxiv.1406.2545
Most community detection algorithms from the literature work as optimization tools that minimize a given 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.