Graph learning plays a crucial role in capturing valuable information from both graph structures and node attributes. Despite their success, existing methods struggle to generalize to unseen datasets or classes. To achieve robust generalization, we identify two essential capabilities a graph model should possess: (1) Z...
Yang Liu, Yuan Fang, Deyu Bo et al.· Proceedings of the 35th ACM...· 0 citations
Graph learning plays a crucial role in capturing valuable information from both graph structures and node attributes. Despite their success, existing methods struggle to generalize to unseen datasets or classes. To achieve robust generalization, we identify two essential capabilities a graph model should possess: (1) Z...
Yang Liu, Yuan Fang, Deyu Bo et al.· Proceedings of the 35th ACM...· 0 citations
SAHG (Sector-Anisotropic Hyperbolic Graph) learns a direction-dependent curvature field $\gamma(u)$ that adapts geometric resolution across structural directions, and uses sector prototypes to convert angular concentration and alignment into classifier-readable features.
Han-Ning Lu, Ying-Guang Yang, Jinwei Su et al.· arXiv.org· 1 citation
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