Abstract: A temporal-aware or permutation-dependent Graph Neural Network (GNN) is disclosed. The example GNN is implemented by combining temporal-awareness with multi-layer neighborhood aggregation to further provide the GNN with inductive capabilities with respect to generating embeddings of a dynamic graph, all without creating multiple time snapshots of the graph. By using a temporal-aware message pass scheme involving a temporal-aware and permutation-dependent GNN, a set of temporal-aware local neighborhood aggregator functions may be effective trained and used for generating embeddings for unknow nodes and for providing more accurate embeddings for subsequent prediction tasks.