论文标题
改进的光谱聚类方法，用于在学位校正的随机块模型下进行社区检测
An improved spectral clustering method for community detection under the degree-corrected stochastic blockmodel
论文作者
论文摘要
对于社区检测问题，光谱聚类是一种用于检测网络中簇的广泛使用方法。在本文中，我们提出了在程度校正的随机块模型（DCSBM）下改进的光谱聚类（ISC）方法。 ISC是基于正规化laplacian矩阵的加权领导k + 1特征向量的K均值聚类算法设计的，其中权重为相应的特征值。 ISC的理论分析表明，在轻度条件下，ISC产生稳定的一致的社区检测。数值结果表明，在模拟和八个经验网络上，ISC优于经典光谱聚类方法，用于社区检测。特别是，ISC对两个弱信号网络Simmons和Caltech提供了显着改善，错误率分别为121/1137和96/590。
For community detection problem, spectral clustering is a widely used method for detecting clusters in networks. In this paper, we propose an improved spectral clustering (ISC) approach under the degree corrected stochastic block model (DCSBM). ISC is designed based on the k-means clustering algorithm on the weighted leading K + 1 eigenvectors of a regularized Laplacian matrix where the weights are their corresponding eigenvalues. Theoretical analysis of ISC shows that under mild conditions the ISC yields stable consistent community detection. Numerical results show that ISC outperforms classical spectral clustering methods for community detection on both simulated and eight empirical networks. Especially, ISC provides a significant improvement on two weak signal networks Simmons and Caltech, with error rates of 121/1137 and 96/590, respectively.
