论文标题
改进的光谱聚类方法，用于混合会员社区检测
An improved spectral clustering method for mixed membership community detection
论文作者
论文摘要
近年来，社区发现进行了很好的研究，但是更现实的会员社区发现的案例仍然是一个挑战。在这里，我们开发了一种有效的光谱算法混合ISC，基于将K特征向量应用于聚类的更多，因为K群社区估算了根据学位校正的混合成员资格（DCMM）模型估算社区成员资格。我们表明该算法在渐近上是一致的。在模拟网络和许多经验网络上进行的数值实验表明，混合ISC的性能与许多用于混合成员社区检测的基准方法相比表现良好。特别是，混合ISC在弱信号网络上提供令人满意的性能。
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.
