Mixed membership community detection is a challenge problem in network analysis. Here, under the degree-corrected mixed membership (DCMM) model, we propose an efficient approach called mixed regularized spectral clustering (Mixed-RSC for short) to estimate the memberships. Mixed-RSC is an extension of the RSC method (Qin and Rohe, 2013) to deal with the mixed membership community detection problem. We show that the algorithm is asymptotically consistent under mild conditions. The approach is successfully applied to a small scale of simulations and substantial empirical networks with encouraging results compared to a number of benchmark methods.

中文翻译：

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通过混合正则谱聚类估计网络成员

混合成员社区检测是网络分析中的一个难题。在这里，在度校正的混合隶属度（DCMM）模型下，我们提出了一种有效的方法，称为混合正则化谱聚类（简称Mixed-RSC）来估计隶属度。混合RSC是RSC方法的扩展（Qin和Rohe，2013），用于处理混合成员社区检测问题。我们表明该算法在温和条件下是渐近一致的。与许多基准方法相比，该方法已成功应用于小规模的模拟和大量的经验网络，其结果令人鼓舞。