Enhancing Transformer with GNN Structural Knowledge via Distillation: A Novel Approach

• URL: http://arxiv.org/abs/2503.01888v1
• Date: Thu, 27 Feb 2025 05:14:47 GMT
• Title: Enhancing Transformer with GNN Structural Knowledge via Distillation: A Novel Approach
• Authors: Zhihua Duan, Jialin Wang,
• Abstract summary: This paper proposes a novel knowledge distillation framework that transfers multiscale structural knowledge from GNN teacher models to Transformer student models.<n>The framework effectively bridges the architectural gap between GNNs and Transformers through micro-macro distillation losses and multiscale feature alignment.
• Score: 1.4582633500696451
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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