2019/08/01 by Ting Li, Li, Ting, Xianshi Yu +3
Computer Science · Physics and Astronomy · #Advanced Clustering Algorithms Research #Applications (stat.AP) #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Network Security and Intrusion Detection #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1908.00523
openalex publication_date 2019/08/01 · openalex created_date 2019/08/13 · openalex updated_date 2026/07/28
In this paper, we propose a novel statistic of networks, the normalized clustering coefficient, which is a modified version of the clustering coefficient that is robust to network size, network density and degree heterogeneity under different network generative models. In particular, under the degree corrected block model (DCBM), the "in-out-ratio" could be inferred from the normalized clustering coefficient. Asymptotic properties of the proposed indicator are studied under three popular network generative models. The normalized clustering coefficient can also be used for networks clustering, network sampling as well as dynamic network analysis. Simulations and real data analysis are carried out to demonstrate these applications.