Abstract: In an embodiment, operations include receiving a first graph associated with a first task following graph learning tasks including a sequence of second graphs. A set of sample graphs is selected from a set of condensed graph distributions (CGDs) associated with the graph learning tasks. A set of statistics associated with the set of CGDs is updated, based on one or more auxiliary graph neural network (GNN) models, the first graph, and the set of sample graphs. A first CGD associated with the first task is learned. A plurality of sample graphs is re-selected from the first CGD and the set of CGDs. A first loss corresponding to a prediction error associated with a downstream prediction task of the primary GNN model is determined. A prediction result associated with the downstream prediction task is generated by the primary GNN model, based on the first loss.

Abstract: A computer-implemented method comprising performing a training process, the training process comprising: using a student graph neural network, GNN, to extract a pair of first representations from a pair of training graphs, respectively; computing a disagreement between the first representations; using at least one teacher GNN to extract a pair of second representations from the pair of training graphs, respectively; computing a perceptual distance between the second representations; comparing the disagreement between the first representations with a target disagreement which is based at least in part on the perceptual distance between the second representations; and adjusting at least one weight of the student GNN based on the comparison.