报告题目：Community influence analysis in social network

报告人：方匡南

时  间：07月27日（周二）下午3:00-5:00

地  点：雁塔校区财经主楼802学术报告厅

报告人简介：

方匡南, 厦门大学经济学院教授、博士生导师、耶鲁大学博士后、厦门大学信用大数据与智能风控研究中心主任、厦门大学数据挖掘研究中心副主任、国际统计学会 elected member、中国商业统计学会常务理事、全国工业统计学会常务理事、数据科学与商业智能学会常务理事、厦门统计学会常务理事等。入选中组部国家“万人计划”青年拔尖人才、福建省“特支双百计划”青年拔尖人才、厦门大学南强青年拔尖人才（A类）、福建省高校杰出青年科研人才培育计划、福建省高校新世纪优秀人才支持计划。曾先后发表论文90多篇，其中SCI/SSCI 50多篇，在国际权威期刊发表40多篇，在《管理科学学报》、《经济研究》、《统计研究》、《数量经济技术经济研究》等国内权威期刊发表40多篇。先后主持了国家自然科学基金面上项目、青年项目、国家社科基金重大项目子课题、国家统计局重大项目等学术纵向课题10多项。

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.

赌博平台

2021年7月23日