Evolutionary multi-objective attribute community detection based on similarity fusion strategy with central nodes

In recent years, evolutionary multi-objective based community detection algorithms are widely used in attribute networks, but such algorithms usually ignore the attributes between nodes and may lead to incorrect node division. Therefore, this paper proposes an evolutionary multi-objective attribute community detection based on similarity fusion strategy with central nodes. First, this paper proposes a pre-processing of similarity fusion to completely utilize node and topology information, the topological similarity matrix of the network is effectively combined with the attribute similarity matrix to obtain the fusion similarity matrix, and finds central nodes based on node assignment of the fusion similarity matrix. Then, the pre-division set of the attribute network is selected by central nodes and the label update equation is designed. In the scheme of evolutionary algorithm, using the community results initialized for the network after the label update can speed up the iterative process of the algorithm. Finally, a modularity-based community integration strategy is proposed to correct community detection results of attribute network based on modularity of neighbor nodes. Comparing the effectiveness of the proposed algorithm with four excellent community detection algorithms for attribute networks on fifteen real networks and six synthetic networks, the proposed algorithm can achieve high division accuracy in most networks.