2020/12/09 by Huan Qing, Qing, Huan, Jingli Wang +1
Computer Science · Physics and Astronomy · #Advanced Clustering Algorithms Research #Complex Network Analysis Techniques #FOS: Computer and information sciences #Social and Information Networks (cs.SI) #Web Data Mining and Analysis #cs.SI
paper · pdf · doi:10.48550/arxiv.2012.04867
24 pages, 2 figures, 14 tables. arXiv admin note: substantial text overlap with arXiv:2011.12239
openalex publication_date 2020/12/09 · arxiv created 2020/12/11 · arxiv updated 2020/12/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Community detection has been well studied recent years, but the more realistic case of mixed membership community detection remains a challenge. Here, we develop an efficient spectral algorithm Mixed-ISC based on applying more than K eigenvectors for clustering given K communities for estimating the community memberships under the degree-corrected mixed membership (DCMM) model. We show that the algorithm is asymptotically consistent. Numerical experiments on both simulated networks and many empirical networks demonstrate that Mixed-ISC performs well compared to a number of benchmark methods for mixed membership community detection. Especially, Mixed-ISC provides satisfactory performances on weak signal networks.