Identifying overlapping communities in complex networks with multimodal optimization

The analysis of complex networks is an important research topic that helps us understand the underlying behavior of complex systems and the interactions of their components. One particularly relevant analysis is the detection of communities formed by such interactions. Most community detection algorithms work as optimization tools that minimize a given quality function, while assuming that each node belongs to a single community. However, most complex networks contain nodes that belong to two or more communities, which are called bridges. The identification of bridges is crucial to several problems, as they often play important roles in the system described by the network. By exploiting the multimodality of quality functions, it is possible to obtain distinct optimal communities where, in each solution, each bridge node belongs to a distinct community. This paper proposes a technique that tries to identify a set of (possibly) overlapping communities by combining diverse solutions contained in a pool, which correspond to disjoint community partitions of a given network. To obtain the pool of partitions, an adapted version of the immune-inspired algorithm named cob-aiNet[C] was adopted here. The proposed methodology was applied to four real-world social networks and the obtained results were compared to those reported in the literature. The comparisons have shown that the proposed approach is competitive and even capable of overcoming the best results reported for some of the problems.