DRSA provides a plug-and-play alignment framework that decouples features and relations to prevent type collapse and relation confusion in heterogeneous graph foundation models.
Heteroge- neous graph neural network,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
TypeBandit allocates a global sampling budget at the node-type level via bandits to supply type summaries as contextual signals for attribute completion, delivering dataset-dependent gains when plugged into standard heterogeneous GNNs like R-GCN and HGT.
citing papers explorer
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Empowering Heterogeneous Graph Foundation Models via Decoupled Relation Alignment
DRSA provides a plug-and-play alignment framework that decouples features and relations to prevent type collapse and relation confusion in heterogeneous graph foundation models.
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TypeBandit: Type-Level Context Allocation and Reweighting for Effective Attribute Completion in Heterogeneous Graph Neural Networks
TypeBandit allocates a global sampling budget at the node-type level via bandits to supply type summaries as contextual signals for attribute completion, delivering dataset-dependent gains when plugged into standard heterogeneous GNNs like R-GCN and HGT.