Large-scale graph language models acquire structural regularities beyond memorization, with subgraph rank correlations persisting after bootstrap and novel-subset controls, especially for high-frequency patterns.
Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, and Jure Leskovec
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When Graph Language Models Go Beyond Memorization
Large-scale graph language models acquire structural regularities beyond memorization, with subgraph rank correlations persisting after bootstrap and novel-subset controls, especially for high-frequency patterns.