SpecSphere: Dual-Pass Spectral-Spatial Graph Neural Networks with Certified Robustness

• URL: http://arxiv.org/abs/2505.08320v2
• Date: Wed, 14 May 2025 17:07:37 GMT
• Title: SpecSphere: Dual-Pass Spectral-Spatial Graph Neural Networks with Certified Robustness
• Authors: Yoonhyuk Choi, Chong-Kwon Kim,
• Abstract summary: We introduce SpecSphere, the first dual-pass spectral-spatial GNN that certifies every prediction against both $ell_0$ edge flips and $ell_inftyversa feature perturbations.<n>Our model couples a Chebyshev-polynomial spectral branch with an attention-gated spatial branch and fuses their representations through a lightweight trained in a cooperative-adrial min-max game.
• Score: 1.7495213911983414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce SpecSphere, the first dual-pass spectral-spatial GNN that certifies every prediction against both $\ell\_{0}$ edge flips and $\ell\_{\infty}$ feature perturbations, adapts to the full homophily-heterophily spectrum, and surpasses the expressive power of 1-Weisfeiler-Lehman while retaining linear-time complexity. Our model couples a Chebyshev-polynomial spectral branch with an attention-gated spatial branch and fuses their representations through a lightweight MLP trained in a cooperative-adversarial min-max game. We further establish (i) a uniform Chebyshev approximation theorem, (ii) minimax-optimal risk across the homophily-heterophily spectrum, (iii) closed-form robustness certificates, and (iv) universal approximation strictly beyond 1-WL. SpecSphere achieves state-of-the-art node-classification accuracy and delivers tighter certified robustness guarantees on real-world benchmarks. These results demonstrate that high expressivity, heterophily adaptation, and provable robustness can coexist within a single, scalable architecture.

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