GGDA framework generates knowledge-preserving intermediate graphs via FGW metric and a vertex-based progression to enable gradual domain adaptation across large graph distribution shifts.
Pairwise alignment improves graph domain adaptation
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DIB-OD isolates a stable invariant core in heterogeneous graph representations via orthogonal subspace decomposition, IB teacher-student distillation, HSIC independence, and confidence-gated regularization for improved cross-domain generalization.
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Gradual Domain Adaptation for Graph Learning
GGDA framework generates knowledge-preserving intermediate graphs via FGW metric and a vertex-based progression to enable gradual domain adaptation across large graph distribution shifts.
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DIB-OD: Preserving the Invariant Core for Robust Heterogeneous Graph Adaptation via Decoupled Information Bottleneck and Online Distillation
DIB-OD isolates a stable invariant core in heterogeneous graph representations via orthogonal subspace decomposition, IB teacher-student distillation, HSIC independence, and confidence-gated regularization for improved cross-domain generalization.