报告题目：A generative model for exploring structure regularities in attributed networks
主 讲 人：常振海（副教授）
时 间：2020年9月 11日15：00-16：00
平 台: 腾讯会议
会 议 号：939 243 580
密 码：0911
报告摘要：Many real-world networks known as attributed networks contain two types of information: topology information and node attributes. It is a challenging task on how to use these two types of information to explore structural regularities. Here, by characterizing the potential relationship between communities of links and node attributes, a principled statistical model named PSB_PG that generates link topology and node attributes is proposed. This model for generating links is based on the stochastic blockmodels following a Poisson distribution. Therefore, it is capable of detecting a wide range of network structures including community structures, bipartite structures, and other mixture structures. The model for generating node attributes assumes that node attributes are high-dimensional, sparse, and also follow a Poisson distribution. This makes the model be uniform, and the model parameters can be directly estimated by the expectation-maximization (EM) algorithm. Experimental results on artificial networks and real net- works containing various structures have shown that the proposed model PSB_PG is not only competitive with the state-of-the-art models, but also provides a good semantic interpretation for each community via the learned relationship between the community and its related attributes.
主讲人简介：常振海，男，博士（中央财经大学统计学院数理统计专业），现为天水师范学院数学与统计学院教师，副教授，主要研究方向为：聚类分析、复杂网络分析、统计推断等。目前已在Physica A, Information Sciences等国际国内学术期刊发表论文近20篇。
重庆三峡学院数学与统计学院
重庆三峡学院三峡大数据学院
2020年9月9日
