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[2504.10917] Towards A Universal Graph Structural Encoder
Summary
The paper presents GFSE, a universal graph structural encoder designed to capture transferable structural patterns across various graph domains, enhancing performance in downstream tasks.
Why It Matters
As graph-based data becomes increasingly prevalent across various fields, developing a model that can generalize across different graph structures is crucial. GFSE addresses the limitations of existing models by providing a robust framework for encoding complex graph topologies, which could significantly improve applications in social networks, citation analysis, and more.
Key Takeaways
- GFSE is the first cross-domain graph structural encoder pre-trained with multiple self-supervised learning objectives.
- The model utilizes a Graph Transformer architecture, enhancing its ability to capture intricate topological features.
- GFSE integrates seamlessly with various downstream encoders, including graph neural networks and LLMs.
- Experiments show GFSE improves model performance while reducing the need for extensive fine-tuning.
- This advancement could lead to better applications in diverse fields relying on graph data.
Computer Science > Machine Learning arXiv:2504.10917 (cs) [Submitted on 15 Apr 2025 (v1), last revised 22 Feb 2026 (this version, v2)] Title:Towards A Universal Graph Structural Encoder Authors:Jialin Chen, Haolan Zuo, Haoyu Peter Wang, Siqi Miao, Pan Li, Rex Ying View a PDF of the paper titled Towards A Universal Graph Structural Encoder, by Jialin Chen and 5 other authors View PDF HTML (experimental) Abstract:Recent advancements in large-scale pre-training have shown the potential to learn generalizable representations for downstream tasks. In the graph domain, however, capturing and transferring structural information across different graph domains remains challenging, primarily due to the inherent differences in graph topological patterns across various contexts. For example, a social network's structure is fundamentally different from that of a product co-purchase graph. Additionally, most existing models struggle to capture the rich topological complexity of graph structures, leading to inadequate exploration of the graph embedding space. To address these challenges, we propose GFSE, a universal pre-trained graph encoder designed to capture transferable structural patterns across diverse domains such as the web graph, social networks, and citation networks. GFSE is the first cross-domain graph structural encoder pre-trained with multiple self-supervised learning objectives. Built on a Graph Transformer, GFSE incorporates attention mechanisms informed by graph structu...