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Graph generation is a critical task in numerous domains, including molecular design and social network analysis, due to its ability to model complex relationships and structured data.
Strategies for pre-training graph neural networks, 2020a
Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., and Leskovec, J · 1905
Earlier work this paper cites.
Sun, F.-Y., Hoffmann, J., Verma, V., and Tang, J · 1908
Earlier work this paper cites.
Hierarchical recurrent neural networks for long-term dependencies
Hihi, S. and Bengio, Y · 1995
Earlier work this paper cites.
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You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., and Shen, Y · 2010
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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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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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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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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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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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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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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Earlier work this paper cites.
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Cited alongside, same era.
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Later among the works it cites.
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Later among the works it cites.
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