我院助理教授王武毅的合作论文Strong Consistency of Spectral Clustering for Stochastic Block Models” (合作者： Liangjun Su 、Yichong Zhang) 被 IEEE Transactions on Information Theory接受发表。

Abstract：In this paper we prove the strong consistency of several methods based on the spectral clustering techniques that are widely used to study the community detection problem in stochastic block models (SBMs). We show that under some weak conditions on the minimal degree, the number of communities, and the eigenvalues of the probability block matrix, the K-means algorithm applied to the eigenvectors of the graph Laplacian associated with its first few largest eigenvalues can classify all individuals into the true community uniformly correctly almost surely. Extensions to both regularized spectral clustering and degree-corrected SBMs are also considered. We illustrate the performance of different methods on simulated networks.

个人简介：

王武毅，IESR助理教授，2018年获得新加坡管理大学经济学博士学位。他的研究领域为理论计量经济学、应用计量经济学，研究成果发表于Journal of Applied Econometrics、Economics Letters等国际知名期刊。