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arXiv AI By Yun Young Choi, Asung Kil, Sun Woo Park, Minho Lee, Seokhwan Kim

Learning Pseudo-Riemannian Manifolds for Heterophilic Graphs via Graph Signature

Read the original on arXiv AI →

The paper introduces the Graph Signature Index to quantify the mismatch between positive-definite geometric priors in many Graph Neural Networks and the negative distances often needed for heterophilic graphs. Using this signature, the authors propose the Pseudo‑Riemannian Attention Network (PRAT), which learns a signed squared pseudo‑distance metric that can capture both attractive and repulsive relationships. Experiments on fifteen node‑classification benchmarks show that PRAT matches or surpasses state‑of‑the‑art heterophily‑adapted models, and ablation studies confirm that the sign structure provided by the signature is essential for its performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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