报告时间：2023年9月26日（星期二）14：30-15：10

报告地点：翡翠科教楼B1710

报告人：韩潇 特任教授

工作单位：中国科学技术大学

举办单位：数学学院

报告简介：

In statistical network analysis, we often assume either the full network is available or multiple subgraphs can be sampled to estimate various global properties of the network. However, in a real social network, people frequently make decisions based on their local view of the network alone. Here, we consider a partial information framework that characterizes the local network centered at a given individual by path length L and gives rise to a partial adjacency matrix. Under L = 2, we focus on the problem of (global) community detection using the popular stochastic block model (SBM) and its degree-corrected variant (DCSBM). We derive general properties of the eigenvalues and eigenvectors from the signal term of the partial adjacency matrix and propose new spectral-based community detection algorithms that achieve consistency under appropriate conditions. Our analysis also allows us to propose a new centrality measure that assesses the importance of an individual’s partial information in determining global community structure. Using simulated and real networks, we demonstrate the performance of our algorithms and compare our centrality measure with other popular alternatives to show it captures unique nodal information. Our results illustrate that the partial information framework enables us to compare the viewpoints of different individuals regarding the global structure

报告人简介：

韩潇，中国科学技术大学管理学院特任教授，研究方向为大维随机矩阵；高维统计推断，入选国家创新人才计划青年项目，主持青年科学基金项目。