2016/01/04 by Ju Xiang, Ke Hu, Yan Zhang +7 · 19 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Bioinformatics and Genomic Networks #Community structure #Complex Network Analysis Techniques #Local community #Local structure #Meaning (existential) #Similarity (geometry) #Similarity measure #Structural similarity #cs.SI #physics.data-an #physics.soc-ph
paper · pdf · doi:10.1088/1742-5468/2016/03/033405
published in Journal of Statistical Mechanics Theory and Experiment 2016(3), 033405 (Institute of Physics) · 18 pages, 11figures, 3 tables
arxiv created 2016/01/04 · openalex publication_date 2016/03/24 · arxiv updated 2016/03/25 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
Many real-world networks, such as gene networks, protein–protein interaction networks and metabolic networks, exhibit community structures, meaning the existence of groups of densely connected vertices in the networks. Many local similarity measures in the networks are closely related to the concept of the community structures, and may have a positive effect on community detection in the networks. Here, various local similarity measures are used to extract local structural information, which is then applied to community detection in the networks by using the edge-reweighting strategy. The effect of the local similarity measures on community detection is carefully investigated and compared in various networks. The experimental results show that the local similarity measures are crucial for the improvement of community detection methods, while the positive effect of the local similarity measures is closely related to the networks under study and applied community detection methods.