A local-to-global scheme-based multi-objective evolutionary algorithm for overlapping community detection on large-scale complex networks

Recently, multi-objective evolutionary algorithms (MOEAs) have been shown promising performance for detecting overlapping community structure in complex networks. However, it is still challenging to design MOEAs for overlapping community detection on large-scale complex networks due to the curse of dimensionality. Along this avenue, this paper proposes a local-to-global scheme-based MOEA named LG-MOEA for overlapping community detection on large-scale complex networks, which mainly consists of two stages: a local community structure detection stage and a global community structure determination stage. To be specific, in the local community structure detection stage, the key nodes that are central to community and essential to the connectedness of community are firstly identified. Then for each key node, an MOEA with the proposed community boundary control strategy is suggested to detect a set of local overlapping communities through local expansion around the key node. In the global community structure determination stage, a single objective evolutionary algorithm is adopted to search for a suitable local overlapping community for each key node and combine them as one global community partition of the whole network. The proposed LG-MOEA is compared with several competitive overlapping community detection algorithms on both real-world small-scale and large-scale networks, and the experimental results show its superiority for overlapping community detection in terms of the generalized normalized mutual information gNMI and the extended modularity $$Q_{ov}$$ , especially has competitive superiority for large-scale complex networks.