Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking
This is an incremental survey that organizes existing methods for researchers and practitioners in networking.
The paper surveys machine learning approaches for solving combinatorial optimization problems on graphs, focusing on applications in telecommunications and comparing different learning structures.
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.