This project will develop a graph neural network model that generates a prediction (e.g. detects anomalies) together with a set of explanations as to what the model based its prediction on. The model applies to dynamic graphs (such as wireless communication networks), and the explanations generated are either based on the properties of the graph nodes (e.g. parameters of network cells, such as traffic or power consumption) or the connectivity structure of the node (e.g. the state of the neighboring cells).