学术报告：Subsampling spectral clustering for stochastic block models in large-scale networks

报告时间：2023年11月22日（星期三）下午14:00-15:00

报告地点：沙河校区，二教108

报告人：黄丹阳，中国人民大学统计学院，教授

报告摘要：The rapid development of science and technology has generated large amounts of network data, leading to significant computational challenges for network community detection. A novel subsampling spectral clustering algorithm is proposed to address this issue, which aims to identify community structures in large-scale networks with limited computing resources. The algorithm constructs a subnetwork by simple random subsampling from the entire network, and then extends the existing spectral clustering to the subnetwork to estimate the community labels for entire network nodes. As a result, for large-scale datasets, the method can be realized even using a personal computer. Moreover, the proposed method can be generalized in a parallel way. Theoretically, under the stochastic block model and its extension, the degree-corrected stochastic block model, the theoretical properties of the subsampling spectral clustering method are correspondingly established. Finally, to illustrate and evaluate the proposed method, a number of simulation studies and two real data analyses are conducted.

报告人简介：中国人民大学统计学院教授，博士生导师，应用统计科学研究中心研究员，中国现场统计研究会教育统计与管理分会常务理事，北京大数据协会理事会副秘书长，常务理事。主持国家自然科学基金等多项省部级及以上课题，入选北京市科协青年人才托举工程，曾获北京市优秀人才培养资助。长期从事复杂网络建模、超高维数据分析、分布式计算等方向的理论研究工作，注重统计理论研究在小微企业数字化发展中的实际应用。在Journal of the Royal Statistical Society：Series B (Statistical Methodology),Journal of Econometrics, Journal of Business & Economic Statistics等国内外权威期刊发表论文30余篇，著有教材一部，独立作者专著一部。

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