Learning Combinatorial Optimization on Graphs: A Survey With Applications to Networking

Existing approaches to solving combinatorial optimization problems on graphs suffer from the need to engineer each problem algorithmically, with practical problems recurring in many instances. The practical side of theoretical computer science, such as computational complexity, then needs to be addr...

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Bibliographic Details
Published in:IEEE access Vol. 8; pp. 120388 - 120416
Main Authors: Vesselinova, Natalia, Steinert, Rebecca, Perez-Ramirez, Daniel F., Boman, Magnus
Format: Journal Article
Language:English
Published: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Approximation algorithms
attention mechanisms
Combinatorial analysis
combinatorial optimization
communication networks
Complexity theory
deep learning
graph embeddings
graph neural networks
Graphs
Machine learning
Machine learning algorithms
Optimization
reinforcement learning
resource management
Task analysis
Telecommunications
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Existing approaches to solving combinatorial optimization problems on graphs suffer from the need to engineer each problem algorithmically, with practical problems recurring in many instances. The practical side of theoretical computer science, such as computational complexity, then needs to be addressed. Relevant developments in machine learning research on graphs are surveyed for this purpose. We organize and compare the structures involved with learning to solve combinatorial optimization problems, with a special eye on the telecommunications domain and its continuous development of live and research networks.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3004964