A Multi-Label Propagation Algorithm With the Double-Layer Filtering Strategy for Overlapping Community Detection

Abstract

Overlapping community structures are universal in complex networks. Multi-label propagation approach, as a method for detecting overlapping community, has been received widespread attention for the near linear time complexity. In recent years, some algorithms based on multi-label propagation approach have been proposed, but there is still room to improve the accuracy of these algorithms to detect overlapping communities. In this paper, a double-layer filtering strategy is proposed to help nodes filter labels, and the node centrality of each node according to the number of its neighbors and the number of edges between its neighbors is used to fix the node update sequence. Then based on the double-layer filtering strategy, the new fixed node update sequence and the previous multi-label propagation algorithm, a new algorithm called MLPA-DF is proposed. Theory analysis shows that MLPA-DF still has the near linear time complexity. And experimental results on the synthetic and real-world networks show that MLPA-DF has better performance than previous multi-label propagation algorithms (COPRA, SLPA and LPANNI) in terms of the accuracy of detecting overlapping communities without reducing stability.