GFSE introduces a novel universal graph structural encoder that captures and transfers topological patterns across diverse graph domains.

• Leverages Graph Transformers with innovative positional encoding to model complex graph structures
• Employs contrastive learning to capture both local and global structural information
• Demonstrates superior performance on molecular property prediction and security applications
• Provides domain-agnostic structural representations that work across different graph types

This engineering advancement enables more effective knowledge transfer between graph domains, solving a fundamental challenge in graph representation learning for applications in drug discovery, social network analysis, and cybersecurity.

Towards A Universal Graph Structural Encoder