报告题目：Community influence analysis in social network

报告人：方匡南 教授（厦门大学）

报告时间：2021年11月23日（星期二）19：00

报告地点：腾讯会议 170 505 924

报告摘要：

Heterogeneous influence detection across network nodes is an important task in network analysis. This paper proposes a community influence model (CIM)

by assuming that the nodes can be classified into different communities (i.e., clusters or subgroups) and the nodes within the same community share the common influence parameters. Employing the quasi-maximum likelihood approach, together with the fused lasso-type penalty, we can not only identify the number of communities, but also estimate the influence parameters, without imposing any specific distribution assumption on the error terms. We further demonstrate the resulting estimators enjoy the oracle properties; namely, they perform as well as if the true underlying network structure were given in advance. The proposed approach is also applicable to identify influence nodes under homogeneous setting. To assess the adequacy of the homogeneous influence, the likelihood-ratio type test and its asymptotic theory are established. The performance of our methods is illustrated via simulation studies and an empirical example on coauthor citations for statistical journals.

报告人简介：方匡南，厦门大学经济学院统计学与数据科学系教授、博士生导师，国际统计学会elected member，厦门大学信用大数据与智能风控研究中主任、厦门大学数据挖掘研究中心副主任。主要从事统计机器学习、高维数据分析、经济管理统计、健康医疗大数据等。入选国家级高层次人才计划（中组部）、福建省“特支双百计划”青年拔尖人才、福建省新世纪优秀人才计划、福建省高校杰出青年科研人才培育计划等。兼中国商业统计学会常务理事、全国工业统计教学研究会常务理事、全国中青年统计学家协会常务理事等。先后在国内外权威学术期刊发表了100余篇论文，著有学术专著和教材等6部。主持国家自然科学基金等10多项纵向项目，承担了华为、南方电网、等30多项企事业横向项目。