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.

翻译：在网络分析中,混合成员社区检测是一个挑战性的问题。在经程度修正的混合成员模式下,我们建议一种称为混合常规光谱集聚(混合光谱集)的高效方法来估计成员人数。混合RSC是RSC方法(秦氏和罗河,2013年)的延伸,用于解决混合成员社区检测问题。我们表明算法在温和条件下是无症状的。这种方法成功地应用于小规模的模拟和大量经验网络,与一些基准方法相比,结果令人鼓舞。