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Neural message passing is a basic feature extraction unit for graph-structured data considering neighboring node features in network propagation from one layer to the next.
JWS, The Theory of Sound, vol. 1
Loan Rayleigh · 1945
Earlier work this paper cites.
A microscopic theory for antiphase boundary motion and its application to antiphase domain coarsening
Samuel M Allen and John W Cahn · 1979
Earlier work this paper cites.
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Andrew Kachites McCallum, Kamal Nigam, Jason Rennie, and Kristie Seymore · 2000
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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