Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search
This addresses the challenge of efficiently solving NP-hard problems like satisfiability and social network analysis, offering a learning-based method that is competitive with optimized heuristics.
The paper tackles NP-hard combinatorial optimization problems by combining graph convolutional networks with guided tree search, achieving performance on par with state-of-the-art heuristic solvers and scaling to graphs with up to a hundred thousand nodes.
We present a learning-based approach to computing solutions for certain NP-hard problems. Our approach combines deep learning techniques with useful algorithmic elements from classic heuristics. The central component is a graph convolutional network that is trained to estimate the likelihood, for each vertex in a graph, of whether this vertex is part of the optimal solution. The network is designed and trained to synthesize a diverse set of solutions, which enables rapid exploration of the solution space via tree search. The presented approach is evaluated on four canonical NP-hard problems and five datasets, which include benchmark satisfiability problems and real social network graphs with up to a hundred thousand nodes. Experimental results demonstrate that the presented approach substantially outperforms recent deep learning work, and performs on par with highly optimized state-of-the-art heuristic solvers for some NP-hard problems. Experiments indicate that our approach generalizes across datasets, and scales to graphs that are orders of magnitude larger than those used during training.