Graph Neural Networks and Boolean Satisfiability
  In this paper we explore whether or not deep neural architectures can learn
to classify Boolean satisfiability (SAT). We devote considerable time to
discussing the theoretical properties of SAT. Then, we define a graph
representation for Boolean formulas in conjunctive normal form, and train
neural classifiers over general graph structures called Graph Neural Networks,
or GNNs, to recognize features of satisfiability. To the best of our knowledge
this has never been tried before. Our preliminary findings are potentially
profound. In a weakly-supervised setting, that is, without problem specific
feature engineering, Graph Neural Networks can learn features of
satisfiability.
