  Model Checking is widely applied in verifying complicated and especially
concurrent systems. Despite of its popularity, model checking suffers from the
state space explosion problem that restricts it from being applied to certain
systems, or specifications. Many works have been proposed in the past to
address the state space explosion problem, and they have achieved some success,
but the inherent complexity still remains an obstacle for purely symbolic
approaches. In this paper, we propose a Graph Neural Network (GNN) based
approach for model checking, where the model is expressed using a B{\"u}chi
automaton and the property to be verified is expressed using Linear Temporal
Logic (LTL). We express the model as a GNN, and propose a novel node embedding
framework that encodes the LTL property and characteristics of the model. We
reduce the LTL model checking problem to a graph classification problem, where
there are two classes, 1 (if the model satisfies the specification) and 0 (if
the model does not satisfy the specification). The experimental results show
that our framework is up to 17 times faster than state-of-the-art tools. Our
approach is particularly useful when dealing with very large LTL formulae and
small to moderate sized models.
