This tutorial presents a comprehensive overview of three emerging and synergistic directions for tackling distribution shifts in graph learning, which highlight Graph LLMs, which combine the representational power of large language models with graph structures to enable flexible, in-context, and few-shot learning on gr...
Xin Wang, Haoyang Li, Haibo Chen et al.· Proceedings of the 32nd ACM...· 0 citations
Graph machine learning has witnessed rapid progress across both academia and industry. However, most existing methods are developed under the in-distribution (I.D.) hypothesis, which assumes that training and testing graph data are drawn from the same distribution. In real-world applications—ranging from dynamic knowle...
Xin Wang, Haoyang Li, Haibo Chen et al.· Proceedings of the 32nd ACM...· 0 citations
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