SPMGAE: Self-purified masked graph autoencoders release robust expression power
文献类型：期刊论文
| 作者 | Song, Shuhan1,2; Li, Ping1,2; Dun, Ming1; Zhang, Yuan1; Cao, Huawei1,3; Ye, Xiaochun1 |
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2025 |
| 卷号 | 611页码:14 |
| 关键词 | Graph neural networks Masked graph autoencoders Robustness Graph adversarial attacks |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2024.128631 |
| 英文摘要 | To tackle the scarcity of labeled graph data, graph self-supervised learning (SSL) has branched into two paradigms: Generative methods and Contrastive methods. Inspired by MAE and BERT in computer vision (CV) and natural language processing (NLP), masked graph autoencoders (MGAEs) are gaining popularity in the generative genre. However, prevailing MGAEs are mostly designed under the assumption that the data has high homophilic score and is out of adversarial distortion. When people deliberately improve the performance on homophilic graph datasets, they ignore a critical issue that both internal heterophily and artificial attack noise are quite common in the real world. Therefore, when data itself is highly heterophilic or confronted with attacks, they merely have no defensive capability. Especially under self-supervised conditions, it is much more difficult to detect internal heterophily and resist artificial attacks. In this paper, we propose a Self-Purified Masked Graph Autoencoder (SPMGAE) to make up for the shortcomings of prevailing MGAEs in terms of robustness. SPMGAE first utilizes a self-purified module to prune raw graph data and separate perturbation information. The purified graph provides a robust graph structure for the entire pre-training process. Next, the encoding module reuses perturbation information for auxiliary training to enhance robustness, while the decoding module reconstructs the effective graph data at a finer granularity. Extensive experiments on homophilic and heterophilic datasets attacked by various attack methods demonstrate SPMGAE has a considerable robust expressive ability. Especially on small datasets with large perturbations, the improvement of defensive performance could reaches 10%-25%. |
| 资助项目 | National Key Research and Development Program[2023YFB4502305] ; Beijing Natural Science Foundation[4232036] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001327273600001 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/39571]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Cao, Huawei |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Processors, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Zhongguancun Lab, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Song, Shuhan,Li, Ping,Dun, Ming,et al. SPMGAE: Self-purified masked graph autoencoders release robust expression power[J]. NEUROCOMPUTING,2025,611:14. |
| APA | Song, Shuhan,Li, Ping,Dun, Ming,Zhang, Yuan,Cao, Huawei,&Ye, Xiaochun.(2025).SPMGAE: Self-purified masked graph autoencoders release robust expression power.NEUROCOMPUTING,611,14. |
| MLA | Song, Shuhan,et al."SPMGAE: Self-purified masked graph autoencoders release robust expression power".NEUROCOMPUTING 611(2025):14. |
入库方式： OAI收割
来源：计算技术研究所
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