Automated Refutation with Monte Carlo Search of Graph Theory Conjectures on the Maximum Laplacian Eigenvalue

Liora Taieb, Milo Roucairol, Tristan Cazenave, Ararat Harutyunyan

Confirmation: I have read and agree with the workshop's policy on behalf of myself and my co-authors.

Tracks: Main Track

Keywords: Monte Carlo Search, Spectral Graph Theory, Conjecture, Refutation

TL;DR: We refute spectral graph theory conjectures at an unprecedented speed and efficiency using MCTS with graph growth.

Abstract: We address the problem of automatic refutation of spectral graph theory conjectures with Monte Carlo methods. Usual ways are testing conjectures on an exhaustive database of graphs below a certain size, local search algorithms, or, more recently, deep reinforcement learning. We expand on previous works by finding smaller (and often sparser) counter-examples to spectral graph theory conjectures in seconds when it takes minutes or hours with other methods. We apply search algorithms (including state-of-the-art Monte Carlo Searches) to 68 automated conjectures already addressed by the deep cross-entropy method. In addition to the ones already disproved by deep cross-entropy, we refute 2 open conjectures until now. We highlight the efficiency of Monte Carlo Search algorithms compared to a state-of-the-art neural approach, and the advantages of the constructive method. Monte Carlo search can be used to automatically refute conjectures that are experimentally generated.

Submission Number: 9