Multi-objective Overlapping Community Detection by Global and Local Approaches

Overlapping community detection on social networks has received a lot of attention nowadays and it has been recently addressed as Multi-objective Optimization Evolutionary Algorithms. In this paper, we introduce a new algorithm, named MOGLAOC, which is based on the Pareto-dominance based MOEAs and combines global and local approaches for discovering overlapping communities. The experimental evaluation over four classical real-life networks showed that our proposal is promising and effective for overlapping community detection in social networks.

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