Estimating Mixed-Memberships Using the Symmetric Laplacian Inverse Matrix
This work provides an improved method for mixed membership community detection, which is beneficial for researchers and practitioners working with complex network data.
This paper addresses the challenge of mixed membership community detection by introducing Mixed-SLIM, a spectral clustering method operating on the symmetrized Laplacian inverse matrix. The proposed methods demonstrate superior performance compared to state-of-the-art techniques in both simulations and real-world datasets for community detection and mixed membership community detection.
Mixed membership community detection is a challenging problem. In this paper, to detect mixed memberships, we propose a new method Mixed-SLIM which is a spectral clustering method on the symmetrized Laplacian inverse matrix under the degree-corrected mixed membership model. We provide theoretical bounds for the estimation error on the proposed algorithm and its regularized version under mild conditions. Meanwhile, we provide some extensions of the proposed method to deal with large networks in practice. These Mixed-SLIM methods outperform state-of-art methods in simulations and substantial empirical datasets for both community detection and mixed membership community detection problems.