An improved spectral clustering method for mixed membership community detection

Abstract

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.

Community detection in attributed collaboration network for statisticians

“…In a similar perspective, Jin (2016), Gel et al (2017) and Qing and Wang (2020) also studied collaboration networks of statisticians. Our analysis contributes in the following perspectives.…”