New HeTGB benchmark evaluates GNNs on heterophilic text-attributed graphs

Researchers have introduced HeTGB, a new benchmark designed to evaluate graph neural networks (GNNs) and pre-trained language models (PLMs) on heterophilic text-attributed graphs. This benchmark comprises five real-world datasets that combine heterophilic graph structures with rich textual node descriptions. The goal of HeTGB is to facilitate a deeper understanding of how these models perform on graphs where linked nodes have diverse attributes and textual information, addressing a gap in current research. AI

IMPACT This benchmark aims to advance research in graph neural networks and language models by providing a standardized evaluation for heterophilic text-attributed graphs.

RANK_REASON The cluster describes a new academic benchmark for evaluating AI models on a specific type of graph data. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• graph neural networks
• HeTGB
• Hugging Face
• Yuxia Wu

• paper
• other

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