
Replay buffer selection in graph continual learning methods is enhanced by considering both class representativeness and diversity within each class of the replayed nodes, leading to improved model performance.


coremsg

dsl-diversity-enhancement-and-structure-learning-for-rehearsal-based-graph-continual-learning


DSL: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual Learning


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Replay buffer selection is crucial in graph continual learning to prevent overfitting and improve model performance.


dslr-diversity-enhancement-and-structure-learning-for-rehearsal-based-graph-continual-learning


DSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual Learning
