Fetching the paper…
Reading the bibliography…
Combinatorial optimization problems are notoriously challenging for neural networks, especially in the absence of labeled instances.
An efficient graph convolutional network technique for the travelling salesman problem
Chaitanya K Joshi, Thomas Laurent, and Xavier Bresson · 1906
Earlier work this paper cites.
Graph theory and probability
Paul Erdös · 1959
Earlier work this paper cites.
Maxima for graphs and a new proof of a theorem of turán
Theodore S Motzkin and Ernst G Straus · 1965
Earlier work this paper cites.
“neural” computation of decisions in optimization problems
John J Hopfield and David W Tank · 1985
Earlier work this paper cites.
Probabilistic construction of deterministic algorithms: approximating packing integer programs
Prabhakar Raghavan · 1988
Earlier work this paper cites.
Improving the performance of the hopfield-tank neural network through normalization and annealing
David E Van den Bout and TK Miller · 1989
Earlier work this paper cites.
Approximating maximum independent sets by excluding subgraphs
Ravi Boppana and Magnús M Halldórsson · 1992
Earlier work this paper cites.
Issues in using function approximation for reinforcement learning
Sebastian Thrun and Anton Schwartz · 1993
Earlier work this paper cites.
Evolution towards the maximum clique
Immanuel M Bomze · 1997
Earlier work this paper cites.
Spectral graph theory
Fan RK Chung and Fan Chung Graham · 1997
Earlier work this paper cites.
The maximum clique problem
Immanuel M Bomze, Marco Budinich, Panos M Pardalos, and Marcello Pelillo · 1999
Earlier work this paper cites.
Neural networks for combinatorial optimization: a review of more than a decade of research
Kate A Smith · 1999
Earlier work this paper cites.
The probabilistic method
Noga Alon and Joel H Spencer · 2004
Earlier work this paper cites.
Cardinality constrained minimum cut problems: complexity and algorithms
Maurizio Bruglieri, Francesco Maffioli, and Matthias Ehrgott · 2004
Earlier work this paper cites.
A flow-based method for improving the expansion or conductance of graph cuts
Kevin Lang and Satish Rao · 2004
Earlier work this paper cites.
A new model for learning in graph domains
Marco Gori, Gabriele Monfardini, and Franco Scarselli · 2005
Earlier work this paper cites.
How hard is graph isomorphism for graph neural networks?
Andreas Loukas · 2005
Earlier work this paper cites.
Local graph partitioning using pagerank vectors
Reid Andersen, Fan Chung, and Kevin Lang · 2006
Earlier work this paper cites.
Balanced graph partitioning
Konstantin Andreev and Harald Racke · 2006
Earlier work this paper cites.
Benchmarks with hidden optimum solutions for graph problems
K BHOSLIB Xu · 2007
Earlier work this paper cites.
Random constraint satisfaction: Easy generation of hard (satisfiable) instances
Ke Xu, Frédéric Boussemart, Fred Hemery, and Christophe Lecoutre · 2007
Earlier work this paper cites.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
Earlier work this paper cites.
Mining significant graph patterns by leap search
Xifeng Yan, Hong Cheng, Jiawei Han, and Philip S Yu · 2008
Earlier work this paper cites.
A constructive proof of the general lovász local lemma
Robin A Moser and Gábor Tardos · 2010
Earlier work this paper cites.
Submodular approximation: Sampling-based algorithms and lower bounds
Zoya Svitkina and Lisa Fleischer · 2011
Earlier work this paper cites.
Social structure of facebook networks
Amanda L Traud, Peter J Mucha, and Mason A Porter · 2012
Earlier work this paper cites.
Multiclass total variation clustering
Xavier Bresson, Thomas Laurent, David Uminsky, and James Von Brecht · 2013
Earlier work this paper cites.
Fast semidifferential-based submodular function optimization: Extended version
Rishabh Iyer, Stefanie Jegelka, and Jeff Bilmes · 2013
Earlier work this paper cites.
The lovász local lemma–a survey
Mario Szegedy · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
SNAP Datasets: Stanford large network dataset collection
Jure Leskovec and Andrej Krevl · 2014
Cited alongside, same era.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Cited alongside, same era.
Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
Cited alongside, same era.
Deep graph kernels
Pinar Yanardag and SVN Vishwanathan · 2015
Cited alongside, same era.
Neural combinatorial optimization with reinforcement learning
Learning to perform local rewriting for combinatorial optimization
Xinyun Chen and Yuandong Tian · 2019
Later among the works it cites.
Extrapolating paths with graph neural networks
Jean-Baptiste Cordonnier and Andreas Loukas · 2019
Later among the works it cites.
Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
Later among the works it cites.
Exact combinatorial optimization with graph convolutional neural networks
Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, and Andrea Lodi · 2019
Later among the works it cites.
Online vehicle routing with neural combinatorial optimization and deep reinforcement learning
JQ James, Wen Yu, and Jiatao Gu · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Irwan Bello, Hieu Pham, Quoc V Le, Mohammad Norouzi, and Samy Bengio · 2016
Cited alongside, same era.
Learning to branch in mixed integer programming
Elias Boutros Khalil, Pierre Le Bodic, Le Song, George Nemhauser, and Bistra Dilkina · 2016
Cited alongside, same era.
A simple and strongly-local flow-based method for cut improvement
Nate Veldt, David F. Gleich, and Michael W. Mahoney · 2016
Cited alongside, same era.
Learning combinatorial optimization algorithms over graphs
Elias Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, and Le Song · 2017
Cited alongside, same era.
Probability and computing: Randomization and probabilistic techniques in algorithms and data analysis
Michael Mitzenmacher and Eli Upfal · 2017
Cited alongside, same era.
A note on learning algorithms for quadratic assignment with graph neural networks
Alex Nowak, Soledad Villar, Afonso S Bandeira, and Joan Bruna · 2017
Cited alongside, same era.
Mastering chess and shogi by self-play with a general reinforcement learning algorithm
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2017
Cited alongside, same era.
Graph colouring meets deep learning: Effective graph neural network models for combinatorial problems, 2019
Henrique Lemos, Marcelo Prates, Pedro Avelar, and Luis Lamb · 2019
Later among the works it cites.
A deep reinforcement learning algorithm using dynamic attention model for vehicle routing problems
Bo Peng, Jiahai Wang, and Zizhen Zhang · 2019
Later among the works it cites.
Learning to solve np-complete problems: A graph neural network for decision tsp
Marcelo Prates, Pedro HC Avelar, Henrique Lemos, Luis C Lamb, and Moshe Y Vardi · 2019
Later among the works it cites.
Approximation ratios of graph neural networks for combinatorial problems, 2019
Ryoma Sato, Makoto Yamada, and Hisashi Kashima · 2019
Later among the works it cites.
Guiding high-performance sat solvers with unsat-core predictions
Daniel Selsam and Nikolaj Bjørner · 2019
Later among the works it cites.
Discriminative structural graph classification, 2019
Younjoo Seo, Andreas Loukas, and Nathanaël Perraudin · 2019
Later among the works it cites.
Jan Toenshoff, Martin Ritzert, Hinrikus Wolf, and Martin Grohe · 2019
Later among the works it cites.
Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
Later among the works it cites.
Differentiation of blackbox combinatorial solvers
Marin Vlastelica, Anselm Paulus, Vít Musil, Georg Martius, and Michal Rolínek · 2019
Later among the works it cites.
Satnet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
Po-Wei Wang, Priya L Donti, Bryan Wilder, and Zico Kolter · 2019
Later among the works it cites.
What can neural networks reason about?
Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2019
Later among the works it cites.
Experimental performance of graph neural networks on random instances of max-cut
Weichi Yao, Afonso S Bandeira, and Soledad Villar · 2019
Later among the works it cites.
Learning local search heuristics for boolean satisfiability
Emre Yolcu and Barnabas Poczos · 2019
Later among the works it cites.
Fast detection of maximum common subgraph via deep q-learning
Yunsheng Bai, Derek Xu, Alex Wang, Ken Gu, Xueqing Wu, Agustin Marinovic, Christopher Ro, Yizhou Sun, and Wei Wang · 2020
Closest in time.
Can graph neural networks count substructures?
Zhengdao Chen, Lei Chen, Soledad Villar, and Joan Bruna · 2020
Closest in time.
Benchmarking graph neural networks
Vijay Prakash Dwivedi, Chaitanya K Joshi, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2020
Closest in time.
Learn to design the heuristics for vehicle routing problem
Lei Gao, Mingxiang Chen, Qichang Chen, Ganzhong Luo, Nuoyi Zhu, and Zhixin Liu · 2020
Closest in time.
Generalization and representational limits of graph neural networks
Vikas K Garg, Stefanie Jegelka, and Tommi Jaakkola · 2020
Closest in time.
Gurobi optimizer reference manual, 2020
LLC Gurobi Optimization · 2020
Closest in time.
coin-or/cbc: Version 2.10.5, March 2020
johnjforrest, Stefan Vigerske, Haroldo Gambini Santos, Ted Ralphs, Lou Hafer, Bjarni Kristjansson, jpfasano, EdwinStraver, Miles Lubin, rlougee, jpgoncal1, h-i gassmann, and Matthew Saltzman · 2020
Closest in time.
Benchmark data sets for graph kernels, 2020
Kristian Kersting, Nils M. Kriege, Christopher Morris, Petra Mutzel, and Marion Neumann · 2020
Closest in time.
A survey on the expressive power of graph neural networks, 2020
Ryoma Sato · 2020
Closest in time.
It’s not what machines can learn, it’s what we cannot teach
Gal Yehuda, Moshe Gabel, and Assaf Schuster · 2020
Closest in time.