题目：Collaborative Spectral Clustering in Attributed Networks

报告人: 姬朋生 （佐治亚大学统计学系和人工智能研究所）

摘要 : We proposed a novel spectral clustering algorithm for attributed networks, where n nodes split into R non-overlapping communities and each node has a p-dimensional meta covariate from various of formats such as text, image, speech etc.. The connectivity matrix $W_{n \times n}$ is constructed with the adjacent matrix $A_{n \times n}$ and covaraite matrix $X_{n \times p}$, and $W = (1-\alpha)A + \alpha K(X,X')$, where $\alpha \in [0,1]$ is a tuning parameter and $K$ is a Kernel to measure the covariate similarities. We then perform the classical k-means algorithm on the element-wise ratio matrix of the first K leading eigenvector of W. Theoretical and simulation studies showed the consistent performance under both Stochastic Block Model (SBM) and Degree-Corrected Block Model (DCBM), especially in imbalanced networks where most community detection algorithms fail.

报告人简介：姬朋生，佐治亚大学统计学系和人工智能研究所副教授，兼统计系副主任。主要研究方向是网络型数据，机器学习和生物信息学, 获得佐治亚大学M. G. Michael Research Award和Teaching Academy Fellow。代表论文发表在 Journal of Business & Economic Statistics (discussion paper),  Annals of Applied Statistics (discussion paper), Annals of Statistics, Journal of Machine Learning Research, International Joint Conference on Artificial Intelligence,  IEEE Transactions on Pattern Analysis and Machine Intelligence, AAAI Conference on Artificial Intelligence.

报告时间：2024年7月1日（周一）下午4:30-5:30

报告地点：教二楼 627

腾讯会议：740171683

联系人：胡涛 方江学