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arXiv · 2012.04867

An improved spectral clustering method for mixed membership community detection

Huan Qing·Jingli Wang
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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.

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Huan Qing, Jingli Wang. 2020-12-09. An improved spectral clustering method for mixed membership community detection. https://arxiv.org/abs/2012.04867

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