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Classical graph algorithms work well for combinatorial problems that can be thoroughly formalized and abstracted.
Residual or gate? towards deeper graph neural networks for inductive graph representation learning
Huang, B.; and Carley, K. M. 2019 · 1904
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Graph neural networks exponentially lose expressive power for node classification
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A reduction of a graph to a canonical form and an algebra arising during this reduction
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Long short-term memory
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LSTM recurrent networks learn simple context-free and context-sensitive languages
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Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks
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