arXiv Machine Learning
Graph Representational Learning: When Does More Expressivity Hurt Generalization?
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On the Rademacher Complexity of Graph Neural Networks: Unifying Expressivity and Geometry
arXiv:2510. 10101v4 Announce Type: replace Abstract: Understanding the interplay between generalization, expressivity, and the geometry of the input space is a central challenge in graph learning.
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OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data
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Introduction to Graph Neural Networks for Machine Learning Engineers
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Graph Hierarchical Recurrence for Long-Range Generalization
Graph Hierarchical Recurrence (GHR) is a new framework that enhances Graph Neural Networks and Graph Transformers by jointly processing the input graph and a pooled hierarchical abstraction. It addresses the limitation of existing models in handling predictions that depend on correlations between distant graph regions, especially under out-of-range generalization where test instances require interactions beyond training distances. Across many long-range benchmarks, GHR consistently improves performance, achieving state‑of‑the‑art or competitive results on multiple tasks.