ShanghaiTech University Knowledge Management System
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)
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ISSN | 2640-009X |
发表状态 | 已发表 |
DOI | 10.1109/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 |
URL | 查看原文 |
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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