Robust Graph Learning Against Adversarial Evasion Attacks via Prior-Free Diffusion-Based Structure Purification
Track: Graph algorithms and modeling for the Web
Keywords: robust graph learning, adversarial evasion attack, graph structure purification, graph diffuison
TL;DR: We propose DiffSP, a novel diffusion-based robust graph learning framework for prior-free structure purification against evasion attacks.
Abstract: Adversarial evasion attacks pose significant threats to graph learning, with lines of studies that have made progress in improving the robustness of Graph Neural Networks (GNNs) for real-world applications. However, existing works overly rely on priors of clean graphs or attacking strategies, which are often heuristic and not universally consistent. To achieve robust graph learning over different types of evasion attacks and diverse datasets, we investigate this non-trivial problem from a prior-free structure purification perspective. Specifically, we propose a novel **Diff**usion-based **S**tructure **P**urification framework named **DiffSP**, which creatively incorporates the graph diffusion model to learn intrinsic latent distributions of clean graphs and purify the perturbed structures by removing adversaries under the direction of the captured predictive patterns without relying on any pre-defined priors. DiffSP is divided into the forward diffusion process and the reverse denoising process, during which structure purification is achieved. To avoid valuable information loss during the forward process, we propose an LID-driven non-isotropic diffusion mechanism to selectively inject controllable noise anisotropically. To promote semantic alignment between the clean graph and the purified graph generated during the reverse process, we reduce the generation uncertainty by the proposed graph transfer entropy guided denoising mechanism. Extensive experiments on both graph and node classification tasks demonstrate the superior robustness of DiffSP against evasion attacks.
Submission Number: 205
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