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papersSEP 10 04:00 UTC

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.

arXiv Dual-channel graph neural network Maximum clique problem algorithm-selection graph neural networks machine-learning

COVERAGE · 2 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.AIInstance-Aware Algorithm Selection for Maximum Clique via a Dual-Channel Graph Neural Architecture ↗SEP 10 04:00 UTC

arXiv cs.LGInstance-Aware Algorithm Selection for Maximum Clique via a Dual-Channel Graph Neural Architecture ↗SEP 10 04:00 UTC