ADR achieves theoretically zero-forgetting class-incremental graph learning by combining backpropagation adaptation with ridge-regression-based layer-wise merging of GNN linear transformations.
Cglb: Benchmark tasks for continual graph learning
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FDQ improves stability in multimodal graph unlearning by using feature-dimension aware quantile selection to protect sensitive high-dimensional layers while preserving utility and enabling effective forgetting.
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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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Stable Multimodal Graph Unlearning via Feature-Dimension Aware Quantile Selection
FDQ improves stability in multimodal graph unlearning by using feature-dimension aware quantile selection to protect sensitive high-dimensional layers while preserving utility and enabling effective forgetting.