ShanghaiTech University Knowledge Management System
ASGNN: Graph Neural Networks with Adaptive Structure | |
2022-10-03 | |
状态 | 已发表 |
摘要 | The graph neural network (GNN) models have presented impressive achievements in numerous machine learning tasks. However, many existing GNN models are shown to be vulnerable to adversarial attacks, which creates a stringent need to build robust GNN architectures. In this work, we propose a novel interpretable message passing scheme with adaptive structure (ASMP) to defend against adversarial attacks on graph structure. Layers in ASMP are derived based on optimization steps that minimize an objective function that learns the node feature and the graph structure simultaneously. ASMP is adaptive in the sense that the message passing process in different layers is able to be carried out over dynamically adjusted graphs. Such property allows more fine-grained handling of the noisy (or perturbed) graph structure and hence improves the robustness. Convergence properties of the ASMP scheme are theoretically established. Integrating ASMP with neural networks can lead to a new family of GNN models with adaptive structure (ASGNN). Extensive experiments on semi-supervised node classification tasks demonstrate that the proposed ASGNN outperforms the state-of-the-art GNN architectures in terms of classification performance under various adversarial attacks. |
DOI | arXiv:2210.01002 |
相关网址 | 查看原文 |
出处 | Arxiv |
WOS记录号 | PPRN:19449668 |
WOS类目 | Computer Science, Artificial Intelligence |
文献类型 | 预印本 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/348522 |
专题 | 信息科学与技术学院_硕士生 信息科学与技术学院_PI研究组_赵子平组 |
作者单位 | 1.ShanghaiTech Univ, Shanghai, Peoples R China 2.IBM Res, New York City, NY 10003, USA 3.Fudan Univ, Shanghai, Peoples R China |
推荐引用方式 GB/T 7714 | Zhang, Zepeng,Lu, Songtao,Huang, Zengfeng,et al. ASGNN: Graph Neural Networks with Adaptive Structure. 2022. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 |
修改评论
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。