ADR achieves theoretically zero-forgetting class-incremental graph learning by combining backpropagation adaptation with ridge-regression-based layer-wise merging of GNN linear transformations.
Graph continual learning with debiased lossless memory replay
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G2LoRA proposes category-aware gradient projection and magnitude modulation within a unified graph-text alignment objective to mitigate interference and promote transfer in continual learning on text-attributed graphs.
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Analytic Drift Resister for Non-Exemplar Continual Graph Learning
ADR achieves theoretically zero-forgetting class-incremental graph learning by combining backpropagation adaptation with ridge-regression-based layer-wise merging of GNN linear transformations.
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G2LoRA: Gradient Orthogonal Low-Rank Adaptation Framework for Graph Continual Learning on Text-Attributed Graphs
G2LoRA proposes category-aware gradient projection and magnitude modulation within a unified graph-text alignment objective to mitigate interference and promote transfer in continual learning on text-attributed graphs.