通过分类复杂性缓解实现鲁棒图压缩
Robust Graph Condensation via Classification Complexity Mitigation
AI总结：
本文提出MRGC框架，通过引入流形约束模块，提升图压缩在对抗攻击下的鲁棒性。
摘要：
图压缩（GC）因其能够合成更小但信息丰富的图的能力而受到广泛关注。然而，现有研究往往忽视了在原始图被破坏的情况下GC的鲁棒性。在这种情况下，我们观察到GC的性能显著下降，而现有的鲁棒图学习技术只能提供有限的有效性。通过实证研究和理论分析，我们揭示GC本质上是一种内在维度减少的过程，合成一个具有较低分类复杂度的压缩图。尽管这一特性对有效的GC性能至关重要，但它仍然高度易受对抗扰动的影响。为了解决这种脆弱性并提高GC的鲁棒性，我们采用图数据流形的几何视角，并提出一种名为MRGC的新的流形约束鲁棒图压缩框架。具体而言，我们引入了三个图数据流形学习模块，引导压缩图位于一个平滑、低维的流形上，具有最小的类别模糊性，从而保持GC的分类复杂度减少能力，并在普遍对抗攻击下确保鲁棒性能。广泛的实验展示了MRGC在多样攻击场景下的鲁棒性。
英文摘要：
Graph condensation (GC) has gained significant attention for its ability to synthesize smaller yet informative graphs. However, existing studies often overlook the robustness of GC in scenarios where the original graph is corrupted. In such cases, we observe that the performance of GC deteriorates significantly, while existing robust graph learning technologies offer only limited effectiveness. Through both empirical investigation and theoretical analysis, we reveal that GC is inherently an intrinsic-dimension-reducing process, synthesizing a condensed graph with lower classification complexity. Although this property is critical for effective GC performance, it remains highly vulnerable to adversarial perturbations. To tackle this vulnerability and improve GC robustness, we adopt the geometry perspective of graph data manifold and propose a novel Manifold-constrained Robust Graph Condensation framework named MRGC. Specifically, we introduce three graph data manifold learning modules that guide the condensed graph to lie within a smooth, low-dimensional manifold with minimal class ambiguity, thereby preserving the classification complexity reduction capability of GC and ensuring robust performance under universal adversarial attacks. Extensive experiments demonstrate the robustness of \ModelName\ across diverse attack scenarios.
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