GON: End-to-end optimization framework for constraint graph optimization problems

Real-world computational applications often require solving combinatorial optimization problems on graphs, i.e., graph optimization problems (GOPs). An emerging trend is using graph neural networks (GNNs) to tackle GOPs. However, for GOPs with constraints, a great challenge faced by GNNs-based methods is to produce optimal solutions that satisfy the constraints. Existing methods relying on supervised learning require a large amount of labeled …