libmg: A Python library for programming graph neural networks in μG

Abstract

Graph neural networks have proven their effectiveness across a wide spectrum of graph-based tasks. Despite their successes, they share the same limitations as other deep learning architectures and pose additional challenges for their formal verification. To overcome these problems, we proposed a specification language, $\mu G$, that can be used to program graph neural networks. This language has been implemented in a Python library called libmg that handles the definition, compilation, visualization, and explanation of $\mu G$ graph neural network models. We illustrate its usage by showing how it was used to implement a Computation Tree Logic model checker in our previous work, and evaluate its performance on the benchmarks of the Model Checking Contest. In the future, we plan to use $\mu G$ to further investigate the issues of explainability and verification of graph neural networks.