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

2025-02-27

AI Summary

To address Transformers’ lack of graph-structural priors and their difficulty in jointly modeling local topology and long-range dependencies, this paper proposes the first cross-architecture knowledge distillation paradigm tailored for structural knowledge transfer—migrating multi-scale structural inductive biases from GNNs to Transformers. Methodologically, we introduce a micro-macro distillation loss and a multi-scale feature alignment mechanism that jointly aligns structure-aware representations at the node, subgraph, and whole-graph levels. Our contributions are threefold: (1) systematically bridging the architectural gap between GNNs and Transformers; (2) pioneering a structured distillation objective that explicitly encodes graph-structural priors; and (3) significantly enhancing Transformers’ structural awareness across multiple benchmark datasets, achieving synergistic improvements in both local topological pattern capture and long-range dependency modeling.

Technology Category

• Machine Learning: Structured Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Bridging structured and unstructured data

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.

Problem

• Integrate GNN structural biases with Transformer global modeling.
• Address GNN limitations in long-range dependencies and scalability.
• Transfer multiscale structural knowledge from GNNs to Transformers.

Innovation

• Knowledge distillation from GNN to Transformer
• Micro-macro distillation losses for feature alignment
• Integrating GNN structural biases into Transformers