A Joint Graph Signal and Laplacian Denoising Network
2024
会议录名称2024 ASIA PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC)
ISSN2640-009X
发表状态已发表
DOI10.1109/APSIPAASC63619.2025.10849018
摘要

Graph neural network (GNN) models have presented astonishing achievements in various application fields. However, they are shown to be vulnerable to adversarial attacks on graph structure and unnoticeable perturbations on the graph structure can cause significant performance drops in GNN models. Based on recent studies that reveal a class of GNN models is performing graph signal denoising (GSD), in this paper, we design a novel robust GNN model from a joint graph signal and Laplacian denoising problem (GSLD), named GSLDN. Specifically, GSLDN is built based on a block majorization-minimization algorithm for solving the GSLD problem. Designed in such a principled way, GSLDN is endowed with the power to fight against adversarial attacks on graph structure. Experiment results demonstrate the effectiveness of GSLDN.

会议地点Macau, Macao
会议日期3-6 Dec. 2024
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语种英语
来源库IEEE
文献类型会议论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/484008
专题信息科学与技术学院_PI研究组_赵子平组
作者单位
1.École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
2.ShanghaiTech University, China
推荐引用方式
GB/T 7714
Zepeng Zhang,Ziping Zhao. A Joint Graph Signal and Laplacian Denoising Network[C],2024.
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