Evolutionary multi-objective overlapping community detection based on fusion of internal and external connectivity and correction of node intimacy

Abstract

In the field of community detection, node attribute information plays an important role in community division. Existing methods use topology structure and node attribute information to discover non-overlapping communities. However, so far, attribute information has not been fully utilized in overlapping community detection. To address this, we propose a new overlapping community detection method called “evolutionary multi-objective overlapping community detection based on Fusion of internal and external Connectivity and Correction of Node Intimacy” (FCCNI). Firstly, we propose a fusion strategy based on internal and external connectivity, which integrates some communities with sparse intra-connections and dense inter-connections. This automatically determines, reconfirms, and corrects the number of communities. Secondly, a function is designed to calculate the intimacy between nodes, and the node label with the highest intimacy is selected to correct the current wrong node. The correction strategy is used in two stages of initialization and multi-objective evolution to obtain a more accurate node label. Finally, a method that considers not only the connections of the community, but also the node attribute, is designed to obtain the overlapping community indirectly from the non-overlapping community. The experimental results on five real-life networks and four classical synthetic networks show that FCCNI achieves better overlapping community division, compared with six state-of-the-art comparison algorithms from the literature.