Before revision:  We then use the SST framework to create link prediction models for both static and temporal, directed and undirected graphs which produce highly interpretable results that simultaneously match state of the art graph neural network performance .
After revision:  We then use the SST framework to create link prediction models for both static and temporal, directed and undirected graphs which produce highly interpretable results . Quantitatively, our models match out-of-the-box performance of state of the art graph neural network performance .