AI RESEARCH
Stable Multimodal Graph Unlearning via Feature-Dimension Aware Quantile Selection
arXiv CS.LG
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ArXi:2605.03303v1 Announce Type: new Graph unlearning remains a critical technique for ing privacy-preserving and sustainable multimodal graph learning. However, we observe that existing unlearning strategies tend to apply uniform parameter selection and editing across all graph neural network (GNN) layers, which is especially harmful for multimodal graphs where high-dimensional input projections encode dominant cross-modal knowledge.