Graph Neural Reasoning May Fail in Proving Boolean Unsatisfiability
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It is feasible and practically-valuable to bridge the characteristics between graph neural networks (GNNs) and logical reasoning. Despite considerable efforts and successes witnessed in learning Boolean satisfiability (SAT), it remains an open question of learning GNN-based solvers for more complex predicate logic formulae. In this work, we conjectures with theoretically support discussion, that generally defined GNNs present some limitations in reasoning about a set of assignments and proving the unsatisfiability (UNSAT) in Boolean formulae. It implies that GNNs may probably fail in learning the logical reasoning tasks if they contain UNSAT as the sub-problem, thus, included by most of predicate logic reasoning problems.
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