Enhancing Transformer with GNN Structural Knowledge via Distillation: A Novel Approach
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912257556873216 |
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| author | Duan, Zhihua Wang, Jialin |
| author_facet | Duan, Zhihua Wang, Jialin |
| contents | Integrating the structural inductive biases of Graph Neural Networks (GNNs) with the global contextual modeling capabilities of Transformers represents a pivotal challenge in graph representation learning. While GNNs excel at capturing localized topological patterns through message-passing mechanisms, their inherent limitations in modeling long-range dependencies and parallelizability hinder their deployment in large-scale scenarios. Conversely, Transformers leverage self-attention mechanisms to achieve global receptive fields but struggle to inherit the intrinsic graph structural priors of GNNs. This paper proposes a novel knowledge distillation framework that systematically transfers multiscale structural knowledge from GNN teacher models to Transformer student models, offering a new perspective on addressing the critical challenges in cross-architectural distillation. The framework effectively bridges the architectural gap between GNNs and Transformers through micro-macro distillation losses and multiscale feature alignment. This work establishes a new paradigm for inheriting graph structural biases in Transformer architectures, with broad application prospects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01888 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Transformer with GNN Structural Knowledge via Distillation: A Novel Approach Duan, Zhihua Wang, Jialin Machine Learning Artificial Intelligence Integrating the structural inductive biases of Graph Neural Networks (GNNs) with the global contextual modeling capabilities of Transformers represents a pivotal challenge in graph representation learning. While GNNs excel at capturing localized topological patterns through message-passing mechanisms, their inherent limitations in modeling long-range dependencies and parallelizability hinder their deployment in large-scale scenarios. Conversely, Transformers leverage self-attention mechanisms to achieve global receptive fields but struggle to inherit the intrinsic graph structural priors of GNNs. This paper proposes a novel knowledge distillation framework that systematically transfers multiscale structural knowledge from GNN teacher models to Transformer student models, offering a new perspective on addressing the critical challenges in cross-architectural distillation. The framework effectively bridges the architectural gap between GNNs and Transformers through micro-macro distillation losses and multiscale feature alignment. This work establishes a new paradigm for inheriting graph structural biases in Transformer architectures, with broad application prospects. |
| title | Enhancing Transformer with GNN Structural Knowledge via Distillation: A Novel Approach |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2503.01888 |