MoGoCo-@Net: Discrete Multi-Objective Grey Wolf Optimization for Discovering Community Structures in Attributed Networks

In this paper, we deal with the problem of community detection in attributed networks. The multi-objective grey wolf optimizer is adopted in order to discover the optimal partition with multiple objectives for the first time. This paper presents MoGoCo-@Net, a novel multiQbjective discrete grey wolf optimizatlon algorithm to solve the community detection-problem in the @ttributed networks. To fully exploit the topology structure and node attribute of vertices at each time step, we introduce two new criteria and maximize them simultaneously. Moreover, MoGoCo-@Net used opposition-based learning and improved label propagation technique by combining the node's attributes with the network topology for fast and effective initialization. Then, MoGoCo-@Net redefined the social hierarchy and the hunting behavior of grey wolves in discretization. Next, a multi-individual mutation operation is adopted as an evolutionary operation. In our experiments, various benchmark attributed networks are used to compare with some state-of-the-art methods. The experimental results show that the proposed algorithm performs favorably against the compared methods.