报 告 人：亓兴勤 教授（山东大学（威海））
报告题目：Degree-like Centrality with structural zeroes or ones: When is a neighbor not a neighbor?
报告时间：2020年12月2日下午16:00
报告地点：线上：腾讯会议 ID：593 522 591 线下：1508会议室
会议密码：20122
报告人简介：
亓兴勤，山东大学（威海）数学与统计学院教授，博士生导师。目前主要从事图与复杂网络的研究。主要研究兴趣包括复杂网络中重要顶点寻找问题，以及复杂网络中社团结构划分问题。目前为中国运筹学会图论与组合分会理事。
报告摘要：
In the field of social network analysis, identifying influential spreaders (or important vertices) is a significant procedure to understand, control or accelerate the dynamics of information (or disease) diffusion process in complex networks effectively. But there are situations in which researchers hope to ignore certain dyads in the computation of centrality to avoid biased or misleading results, while simply deleting these dyads will result in wrong conclusions. There is little work considering this particular problem except the eigenvector-like centrality method presented in 2015. In this work, we revisit this problem and present a new degree-like centrality method which also allows some dyads to be excluded in the calculations. This new method adopts the technique of weighted symmetric nonnegative matrix factorization (abbreviated as WSNMF), and we will show that it can be seen as the generalized version of the existing eigenvector-like centrality. After applying it to several data sets, we test this new method's efficiency.