关于举行陈艳男副教授（华南师范大学）学术报告的通知

报告题目: Multilinear Pseudo-PageRank for Hypergraph Partitioning

报 告人: 陈艳男 副教授

报告时间: 2023年3月12日（星期日）11:10-11:55    

报告地点：四号楼4318会议室         

邀 请人: 潘少华教授

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数学学院

2023年3月8日

报告摘要：Hypergraphs have the capability of modeling connections among objects according to their inherent multiwise similarity and affinity. Hence, many crucial applications of hypergraph have been mined in science and engineering. In this talk, we build a bridge between uniform hypergraphs and PageRank. Starting from the nonnegative adjacency tensor of a uniform hypergraph, we establish the multilinear pseudo-PageRank model, which is formulated as a multilinear system with nonnegative constraints. The coefficient tensor of the multilinear system is a kind of Laplacian tensor of the uniform hypergraph and no dangling corrections are involved. Then, a gradient projection algorithm is utilized for solving the multilinear pseudo-PageRank problem, of which solutions exist but may not be unique. By using the Lojasiewicz property, we analyze the global and local convergence of the proposed gradient projection algorithm. Numerical experiments illustrate that the proposed multilinear pseudo-PageRank method is powerful and effective for semi-supervised and unsupervised hypergraph partitioning.

报告人简介：陈艳男，博士，华南师范大学副教授。陈博士于2013年在南京师范大学获得博士学位，已发表SCI论文30余篇，代表性论文发表于SIAM J. Matrix Anal. Appl., SIAM J. Sci. Comput., Math. Comput.等国际刊物，参与撰写了一本专著《Tensor Eigenvalues and Their Applications》在Springer出版，完成国家自然科学基金2项，现主持国家自然科学基金面上项目1项，获得2020年度广东省自然科学奖二等奖。