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arXiv Machine Learning By Lorenzo Colantonio (Department of Physics, Sapienza University of Rome), Andrea Cacioppo (Department of Physics, Sapienza University of Rome), Federico Scarpati (Department of Physics, Sapienza University of Rome), Maria Chiara Angelini (Department of Physics, Sapienza University of Rome), Federico Ricci-Tersenghi (Department of Physics, Sapienza University of Rome), Stefano Giagu (Department of Physics, Sapienza University of Rome)

Physics Inspired Neural Networks Enable Efficient Graph Coloring

Read the original on arXiv Machine Learning →

The paper introduces a physics-inspired neural framework that combines graph neural networks with statistical‑mechanics principles to solve large‑scale graph coloring problems. By using a planting‑based supervised signal, symmetry‑breaking regularization, and iterative noise‑annealed neural dynamics, the model navigates complex solution landscapes and scales quadratically with graph size. It generalizes from small training graphs to instances up to 20 times larger, demonstrating that neural architectures can learn scalable algorithmic strategies effective in hard connectivity regions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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