Enhancing Transformer with GNN Structural Knowledge via Distillation: A Novel Approach
This addresses a pivotal problem in graph representation learning for researchers and practitioners by offering a new paradigm to combine architectural strengths, though it is incremental in building on existing distillation methods.
The paper tackles the challenge of integrating GNNs' structural inductive biases with Transformers' global contextual modeling in graph representation learning by proposing a knowledge distillation framework that transfers multiscale structural knowledge from GNNs to Transformers, achieving competitive performance on benchmark datasets.
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.