题  目：Permutation Equivariant Graph Framelets for Heterophilous Graph Learning

内容简介：The nature of heterophilous graphs is significantly different from that of homophilous graphs, which causes difficulties in early graph neural network models and suggests aggregations beyond the 1-hop neighborhood. In this talk, we discuss a new way to implement multi-scale extraction via constructing Haar-type graph framelets with desired properties of permutation equivariance, efficiency, and sparsity, for deep learning tasks on graphs. We design a graph framelet neural network model PEGFAN (Permutation Equivariant Graph Framelet Augmented Network) based on our constructed graph framelets. The experiments are conducted on a synthetic dataset and 9 benchmark datasets to compare performance with other state-of-the-art models. The result shows that our model can achieve the best performance on certain datasets of heterophilous graphs (including the majority of heterophilous datasets with relatively larger sizes and denser connections) and competitive performance on the remaining. This is joint work with Jianfei Li (CityU), Ruigang Zheng (CityU), Han Feng (CityU), and Ming Li (Zhejiang Normal University).

报告人：Xiaosheng Zhuang

报告人简介：Dr. Zhuang Xiaosheng received his bachelor's and master's degrees in mathematics from Sun Yat-sen (Zhongshan) University, China, in 2003 and 2005 respectively. He received his PhD in Applied Mathematics from the University of Alberta, Canada, in 2010. He was a Postdoctoral Fellow at Universität Osnabrück in 2011 and Technische Universität Berlin in 2012. He joined the Department of Mathematics of City University of Hong Kong in 2012 and served as the department Associate Head from 2018 to 2021. He is currently an Associate Professor in the Department of Mathematics, City University of Hong Kong. Dr. Zhuang Xiaosheng's research is mainly on applied and computational harmonic analysis, wavelet and framelet analysis, signal and image processing, deep and machine learning, etc. He has published more than 40 academic papers in international journals, including Applied and Computational Harmonic Analysis (ACHA), SIAM Journal on Imaging Sciences (SIIMS), Mathematics of Computation, ICML, JMLR, IEEE TNNLS, IEEE TIP, Neural Networks, Pattern Recognition, etc

时  间：2024年4月2日（周二）上午10：30开始

地  点：南海楼124室

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信息科学技术学院/网络空间安全学院

2024年3月25日