Network Structural Balance Based on Evolutionary Multiobjective Optimization: A Two-Step Approach


Research on network structural balance has been of great concern to scholars from numerous fields. During this paper, a 2-step approach is proposed for the first time to handle the network structural balance drawback. The proposed approach involves evolutionary multiobjective optimization, followed by model selection. In the primary step, an improved version of the multiobjective discrete particle swarm optimization framework developed in our previous work is urged. The prompt framework is then used to implement network multiresolution clustering. Within the second step, a downside-specific model selection strategy is devised to pick out the most effective Pareto answer (PS) from the Pareto front produced by the first step. The simplest PS is then decoded into the corresponding network community structure. Based mostly on the discovered community structure, imbalanced edges are determined. Afterward, imbalanced edges are flipped thus as to create the network structurally balanced. In depth experiments on synthetic and real-world signed networks demonstrate the effectiveness of the proposed approach.

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