mlsanityData to 7 Oct 2026

Gauge-Equivariant Graph Networks via Self-Interference Cancellation

arXiv 2511.16062 ↗First posted 20 Nov 2025cs.LGcited by 1

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Abstract (arXiv)

Graph Neural Networks (GNNs) excel on homophilous graphs but often fail under heterophily due to self-reinforcing and phase-inconsistent signals. We propose a Gauge-Equivariant Graph Network with Self-Interference Cancellation (GESC), which replaces additive aggregation with a projection-based interference mechanism. Unlike prior magnetic or gauge-equivariant GNNs that rely on additive message mixing, GESC explicitly models self-interference arising from redundant low-frequency components. We show that the absence of interference handling in existing gauge-based GNNs is a primary driver of oversmoothing under gauge transport. We introduce a U(1) phase connection followed by a rank-1 projection that suppresses self-parallel components before attention, and a sign-aware gate that regulates negatively aligned neighbors. Across diverse graph benchmarks, GESC consistently outperforms recent state-of-the-art models while offering a unified, interference-aware view of message passing. Our code is available at https://github.com/ChoiYoonHyuk/GESC.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 56 of its 70 references that are in the map. First posted on arXiv 20 Nov 2025.

Builds on

56 of 70 references.

References · 56

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38 more
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Citing papers per year: 2026: 1.

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