arXiv:2511.19544v1 Announce Type: new
Abstract: Wepropose SplitGNN, a graph neural network (GNN)-based
approach that learns to solve weighted maximum satisfiabil ity (MaxSAT) problem. SplitGNN incorporates a co-training
architecture consisting of supervised message passing mech anism and unsupervised solution boosting layer. A new graph
representation called edge-splitting factor graph is proposed
to provide more structural information for learning, which is
based on spanning tree generation and edge classification. To
improve the solutions on challenging and weighted instances,
we implement a GPU-accelerated layer applying efficient
score calculation and relaxation-based optimization. Exper iments show that SplitGNN achieves 3* faster convergence
and better predictions compared with other GNN-based ar chitectures. More notably, SplitGNN successfully finds solu tions that outperform modern heuristic MaxSAT solvers on
much larger and harder weighted MaxSAT benchmarks, and
demonstrates exceptional generalization abilities on diverse
structural instances.