Combinatorial optimization and reasoning with graph neural networks

By Quentin Cappart, Didier Chételat, Elias Boutros Khalil, Andrea Lodi, Christopher Morris, Petar Velickovic. International Joint Conference on Artificial Intelligence, 2021

498 citations recorded by the source corpus.

Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have mostly focused on solving problem instances in isolation, ignoring the fact that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning, especially graph neural networks, as a key building block for combinatorial tasks, either directly as solvers or by enhancing the former. This paper presents a conceptual review of recent key advancements in this emerging field, aiming at researchers in both optimization and machine learning.

Subfields: Computational Theory and Mathematics, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/d596ac251729fc3647b08b51c5208fdf5414c7c1

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Citations in the corpus

Graph Neural Networks with Learnable Structural and Positional Representations (2021, background): “, 2020), combinatorial optimization (Bengio et al., 2021; Cappart et al., 2021) and medical diagnosis (Li et al.”
Neural algorithmic reasoning (2021, background): “The recently resurging area of algorithmic reasoning [Cappart et al., 2021, Section 3.3.] exactly studies the ways in which such algorithmically-inspired neural networks can be built, primarily through learning to execute the algorithm from abstractified inputs.”