论文标题
使用对称拉普拉斯逆矩阵估算混合成员职位
Estimating Mixed-Memberships Using the Symmetric Laplacian Inverse Matrix
论文作者
论文摘要
混合成员社区发现是一个具有挑战性的问题。在本文中，为了检测混合成员资格，我们提出了一种新方法混合slim，它是对对称的拉普拉斯逆矩阵的光谱聚类方法，该方法是在该学位校正后的混合成员资格模型下。我们为拟议算法及其正则化版本的估计误差提供了理论界限。同时，我们提供了所提出的方法的一些扩展，以处理实践中的大型网络。这些混血方法在模拟和大量经验数据集中的最先进方法都优于社区检测和混合成员社区检测问题。
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.
