Overlapping Structures Detection in Protein-Protein Interaction Networks Using Community Detection Algorithm Based on Neighbor Clustering Coefficient - Wang, Yan; Chen, Qiong; Yang, Lili; Yang, Sen; He, Kai; Xie, Xuping (Frontiers in Genetics, 2021)
With the rapid development of bioinformatics, researchers have applied community detection algorithms to protein-protein interaction (PPI) networks, which help to predict the function of unknown proteins at the molecular level and thereby further reveal the regularity of cell activity. To identify overlapping structures in protein functional modules, this paper proposes an overlapping community detection algorithm based on neighboring local clustering coefficients to select central edges. This algorithm combines the edge-based community detection method with local expansion in seed selection, and the local clustering coefficient of neighboring nodes is introduced to improve the accuracy of seed selection; A method of measuring the distance between edges is improved, which make the result of community division more accurate; A community optimization strategy for the excessive overlapping nodes is proposed, which makes the overlapping structure more reasonable. By comparing this algorithm with classic algorithms on three types of real networks, LFR benchmark networks and PPI networks, the results show that the algorithm proposed in this paper can improve the EQ value and NMI value of the community division, which verifies that the algorithm can not only present a reasonable community structure but also discover overlapping structures in networks.