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.
Graver: Generative graph vocabularies for robust graph foundation models fine-tuning
3 Pith papers cite this work. Polarity classification is still indexing.
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GraphVec produces transferable fixed-dimensional graph embeddings via spectral features from multi-scale global graphs and a convergent mean-alignment procedure, outperforming baselines on cross-domain few-shot classification and clustering across 13 datasets.
UniGraphLM uses a multi-domain multi-task GNN encoder and adaptive alignment to create unified graph tokens for LLMs across diverse domains and tasks.
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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GraphVec: Cross-Domain Graph Vectorization for Graph-Level Representation Learning
GraphVec produces transferable fixed-dimensional graph embeddings via spectral features from multi-scale global graphs and a convergent mean-alignment procedure, outperforming baselines on cross-domain few-shot classification and clustering across 13 datasets.
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A Unified Graph Language Model for Multi-Domain Multi-Task Graph Alignment Instruction Tuning
UniGraphLM uses a multi-domain multi-task GNN encoder and adaptive alignment to create unified graph tokens for LLMs across diverse domains and tasks.