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arXiv AI By Shujie Li, Yuxia Wu, Yuan Fang, Chuan Shi

HeTGB: A Comprehensive Benchmark for Heterophilic Text-Attributed Graphs

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HeTGB is a new benchmark for heterophilic text‑attributed graphs, consisting of five real‑world datasets where nodes have rich textual descriptions. It allows systematic evaluation of graph neural networks, pre‑trained language models, and co‑training methods on node classification. The benchmark highlights the utility of text attributes, the challenges of heterophilic TAGs, and the limitations of current models.

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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