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Neural networks have been shown to be an effective tool for learning algorithms over graph-structured data.
A reduction of a graph to a canonical form and an algebra arising during this reduction
Boris Weisfeiler and AA Lehman · 1968
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Approximation algorithms for combinatorial problems
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Testing for causality: a personal viewpoint
Clive WJ Granger · 1980
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Neural computation of decisions in optimization problems
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Randomized priority algorithms
Spyros Angelopoulos and Allan Borodin · 2003
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(incremental) priority algorithms
Allan Borodin, Morten N Nielsen, and Charles Rackoff · 2003
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Combinatorial optimization: polyhedra and efficiency , volume 24
Alexander Schrijver · 2003
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Marco Gori, Gabriele Monfardini, and Franco Scarselli · 2005
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Arzucan Özgür, Thuy Vu, Güneş Erkan, and Dragomir R Radev · 2008
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2009
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Priority algorithms for graph optimization problems
Allan Borodin, Joan Boyar, Kim S Larsen, and Nazanin Mirmohammadi · 2010
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Graph kernels
S Vichy N Vishwanathan, Nicol N Schraudolph, Risi Kondor, and Karsten M Borgwardt · 2010
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Estimating the directed information to infer causal relationships in ensemble neural spike train recordings
Christopher J Quinn, Todd P Coleman, Negar Kiyavash, and Nicholas G Hatsopoulos · 2011
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Weisfeiler-lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
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The anatomy of the facebook social graph
Johan Ugander, Brian Karrer, Lars Backstrom, and Cameron Marlow · 2011
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The design of approximation algorithms
David P Williamson and David B Shmoys · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann Lecun · 2014
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio · 2014
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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Adam: Amethod for stochastic optimization
Diederik P Kingma and Jimmy Lei Ba · 2015
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Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
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Directed information graphs
Christopher J Quinn, Negar Kiyavash, and Todd P Coleman · 2015
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Improved semantic representations from tree-structured long short-term memory networks
Semantic object parsing with graph lstm
Xiaodan Liang, Xiaohui Shen, Jiashi Feng, Liang Lin, and Shuicheng Yan · 2016
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Learning convolutional neural networks for graphs
Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov · 2016
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Network inference using directed information: The deterministic limit
Arman Rahimzamani and Sreeram Kannan · 2016
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Why should i trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Structured sequence modeling with graph convolutional recurrent networks
Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, and Xavier Bresson · 2016
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A fast heuristic for the minimum weight vertex cover problem
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Kai Sheng Tai, Richard Socher, and Christopher D Manning · 2015
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Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
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Deep graph kernels
Pinar Yanardag and SVN Vishwanathan · 2015
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Diffusion-convolutional neural networks
James Atwood and Don Towsley · 2016
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Neural combinatorial optimization with reinforcement learning
Irwan Bello, Hieu Pham, Quoc V Le, Mohammad Norouzi, and Samy Bengio · 2016
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G: Packing and dependency-aware scheduling for data-parallel clusters
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Community detection with graph neural networks
Joan Bruna and Xiang Li · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Learning combinatorial optimization algorithms over graphs
Elias Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, and Le Song · 2017
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Deriving neural architectures from sequence and graph kernels
Tao Lei, Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2017
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Encoding sentences with graph convolutional networks for semantic role labeling
Diego Marcheggiani and Ivan Titov · 2017
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Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, and Michael M Bronstein · 2017
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A note on learning algorithms for quadratic assignment with graph neural networks
Alex Nowak, Soledad Villar, Afonso S Bandeira, and Joan Bruna · 2017
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Robust spatial filtering with graph convolutional neural networks
Felipe Petroski Such, Shagan Sah, Miguel Dominguez, Suhas Pillai, Chao Zhang, Andrew Michael, Nathan Cahill, and Raymond Ptucha · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2017
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WWM Kool and M Welling · 2018
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