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In this paper, from a theoretical perspective, we study how powerful graph neural networks (GNNs) can be for learning approximation algorithms for combinatorial problems.
Local and global properties in networks of processors (extended abstract)
Dana Angluin · 1980
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Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
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Locality in distributed graph algorithms
Nathan Linial · 1992
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
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Moni Naor and Larry J. Stockmeyer · 1995
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Vijay V. Vazirani · 2001
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Michal Parnas and Dana Ron · 2007
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Fast distributed approximations in planar graphs
Andrzej Czygrinow, Michal Hanckowiak, and Wojciech Wawrzyniak · 2008
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William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Learning combinatorial optimization algorithms over graphs
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struc2vec : Learning node representations from structural identity
Leonardo Filipe Rodrigues Ribeiro, Pedro H. P. Saverese, and Daniel R. Figueiredo · 2017
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Graph attention networks
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How powerful are graph neural networks?
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Graph convolutional neural networks for web-scale recommender systems
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