统计学系系列讲座之332期

 

时间:2019年3月28日(星期四)16:00-17:00

地点:史带楼301室

主持人:张新生 教授 复旦大学管理学院统计学系

主题:Recovering Network Structures Via Security Dimensional Reduction

主讲人:石磊 教授 云南财经大学

简介:石磊教授,云南财经大学博士生导师;教育部**学者特聘教授,国家百千万人才工程人选,国务院特殊津贴获得者,国家有突出贡献中青年专家。现任云南财经大学教授委员会主任、统计与数学学院院长。教育部统计学教学指导委员会委员,全国应用统计专业学位研究生教育指导委员会委员,国家社科基金学科规划评审组专家。中国统计学会、中国现场统计研究会、中国工业与应用数学学会常务理事、中国数量经济学学会副会长。在国内外学术期刊PNAS(美国科学院院刊),Science Advances(Science子刊), Nature Communications,Biometrika, JRS Interface, IEEE系列期刊,《中国科学》等杂志发表论文100余篇;曾获第七届霍英东高校优秀青年教师一等奖,云南省自然科学一、二等奖,云南省科技进步一等奖,中国数量经济学杰出学者奖等20余项奖励。

 

摘要:The curse of dimensionality is a challenging issue in network science: the problem of inferring the network structure from sparse and noisy data becomes more and more difficult, indeed, as their dimensionality increases. We here develop a general strategy for dimensional reduction using iteratively thresholded ridge regression screener, one statistical method aiming to resolve the problem of variable selection. After drastically reducing the dimensions of the problem, we then employ the lasso method, a convex optimization method, to recover the network structure. We demonstrate efficiency of the dimensionality reduction method, and particular suitability for the natural sparsity of complex networks, in which the average degree is much smaller than their total number of nodes. Analysis based on various game dynamics and network topologies show that higher reconstruction accuracies and smaller reconstruction times can be achieved by our method. Our approach provide, therefore, a novel insight to solve the reconstruction problem and has potential applications in a wide range of fields.

                       

 统计学系 

2019-3-26