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Large language models (LLMs) have achieved impressive performance on many natural language processing tasks.
Wordnet: a lexical database for english
George A Miller · 1995
Earlier work this paper cites.
Size and form in efficient transportation networks
Jayanth R Banavar, Amos Maritan, and Andrea Rinaldo · 1999
Earlier work this paper cites.
Random graph models of social networks
Mark EJ Newman, Duncan J Watts, and Steven H Strogatz · 2002
Earlier work this paper cites.
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Earlier work this paper cites.
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Semi-supervised classification with graph convolutional networks
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Earlier work this paper cites.
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Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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Earlier work this paper cites.
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Exploring the potential of large language models (llms) in learning on graphs
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