Dual-channel graph neural network picks the best solver for maximum clique instances
Researchers present a dual-channel graph neural architecture that predicts which exact solver will perform best on a given maximum clique problem instance. Since no single solver dominates across all types of graphs, the approach learns from graph characteristics to make per-instance algorithm choices. The paper appears on arXiv in both the AI and machine learning categories.