Graph Defense Diffusion Model

[2501.11568] Graph Defense Diffusion Model Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2501.11568 (cs) [Submitted on 20 Jan 2025 ( v1 ), last revised 10 Apr 2026 (this version, v2)] Title: Graph Defense Diffusion Model Authors: Xin He , Wenqi Fan , Yili Wang , Chengyi Liu , Rui Miao , Xin Juan , Xin Wang View a PDF of the paper titled Graph Defense Diffusion Model, by Xin He and 6 other authors View PDF HTML (experimental) Abstract: Graph Neural Networks (GNNs) are highly vulnerable to adversarial attacks, which can greatly degrade their performance. Existing graph purification methods attempt to address this issue by filtering attacked graphs. However, they struggle to defend effectively against multiple types of adversarial attacks (e.g., targeted attacks and non-targeted attacks) simultaneously due to limited flexibility. Additionally, these methods lack comprehensive modeling of graph data, relying heavily on heuristic prior knowledge. To overcome these challenges, we introduce the Graph Defense Diffusion Model (GDDM), a flexible purification method that leverages the denoising and modeling capabilities of diffusion models. The iterative nature of diffusion models aligns well with the stepwise process of adversarial attacks, making them particularly suitable for defense. By iteratively adding and removing noises (edges), GDDM effectively purifies attacked graphs, restoring their original structures and features. Our GDDM consists of two key components: (1) Graph Structure-Driven Refiner, which preserves the basic fidelity of the graph during the denoising process, and ensures that the generated graph remains consistent with the original scope; and (2) Node Feature-Constrained Regularizer, which removes residual impurities from the denoised graph, further enhancing the purification effect. By designing tailored denoising strategies to handle different types of adversarial attacks, we improve the GDDM's adaptability to various attack scenarios. Furthermore, GDDM demonstrates strong scalability, leveraging its structural properties to seamlessly transfer across similar datasets without retraining. Extensive experiments on three real-world datasets demonstrate that GDDM outperforms state-of-the-art methods in defending against various adversarial attacks, showcasing its robustness and effectiveness. Comments: Accepted by The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1 (KDD 2026) Subjects: Machine Learning (cs.LG) Cite as: arXiv:2501.11568 [cs.LG] (or arXiv:2501.11568v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2501.11568 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Xin He [ view email ] [v1] Mon, 20 Jan 2025 16:18:40 UTC (20,196 KB) [v2] Fri, 10 Apr 2026 10:01:42 UTC (12,813 KB) Full-text links: Access Paper: View a PDF of the paper titled Graph Defense Diffusion Model, by Xin He and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2025-01 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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