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
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2representative citing papers
citing papers explorer
-
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.
- DIB-OD: Preserving the Invariant Core for Robust Heterogeneous Graph Adaptation via Decoupled Information Bottleneck and Online Distillation