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аналитика - Graph Continual Learning
BeGin: A Comprehensive Benchmark Framework for Graph Continual Learning with Extensive Scenarios
Graph Continual Learning (GCL) research is hindered by the lack of standardized benchmarks and frameworks, a problem addressed by the introduction of BeGin, a new framework offering diverse scenarios and an easy-to-use structure for robust GCL method development and evaluation.
DSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual Learning
Replay buffer selection is crucial in graph continual learning to prevent overfitting and improve model performance.
DSL: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual Learning
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.
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