Condensed Graph Distribution (Cgd) Based Graph Continual Learning

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.

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