Overlapping Structures Detection in Protein-Protein Interaction Networks Using Community Detection Algorithm Based on Neighbor Clustering Coefficient

Abstract

With the rapid development of bioinformatics, researchers have applied community detection algorithms to detect functional modules in protein-protein interaction (PPI) networks that can predict the function of unknown proteins at the molecular level and further reveal the regularity of cell activity. Clusters in a PPI network may overlap where a protein is involved in multiple functional modules. To identify overlapping structures in protein functional modules, this paper proposes a novel overlapping community detection algorithm based on the neighboring local clustering coefficient (NLC). The contributions of the NLC algorithm are threefold: (i) Combine the edge-based community detection method with local expansion in seed selection and the local clustering coefficient of neighboring nodes to improve the accuracy of seed selection; (ii) A method of measuring the distance between edges is improved to make the result of community division more accurate; (iii) A community optimization strategy for the excessive overlapping nodes makes the overlapping structure more reasonable. The experimental results on standard networks, Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks and PPI networks show that the NLC algorithm can improve the Extended modularity (EQ) value and Normalized Mutual Information (NMI) value of the community division, which verifies that the algorithm can not only detect reasonable communities but also identify overlapping structures in networks.

LazyFox: fast and parallelized overlapping community detection in large graphs

“…It measures structural closeness by counting the number of triangles a node forms with a community to determine the membership of that node to that community. Thereby, it is closely related to the general concept of clustering coefficients and has multiple areas of application, e.g ., the analysis of protein-protein interaction networks ( Omranian, Angeleska & Nikoloski, 2021 ; Wang et al, 2021 ), for systemic risk measure in finance ( Cerqueti, Clemente & Grassi, 2021 ), or in trade networks ( Bartesaghi, Clemente & Grassi, 2023 ). In the ‘Methods’ section, we explain this metric in more detail.…”