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- W4366307827 abstract "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." @default.
- W4366307827 created "2023-04-20" @default.
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- W4366307827 date "2022-10-01" @default.
- W4366307827 modified "2023-09-30" @default.
- W4366307827 title "MoGoCo-@Net: Discrete Multi-Objective Grey Wolf Optimization for Discovering Community Structures in Attributed Networks" @default.
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- W4366307827 doi "https://doi.org/10.1109/ictai56018.2022.00174" @default.
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