Hybrid Graph Encoding: A Unified Framework for Adaptive Network Representations

Graph-structured data are increasingly prevalent across diverse computational domains, creating a demand for efficient and scalable embedding techniques. This paper presents a hybrid graph encoding framework that integrates structured sampling with neural propagation to enhance feature extraction from complex network topologies. Using a dual-phase learning mechanism, the approach captures both localized and global structural patterns to produce robust node representations. Extensive evaluations on multiple benchmark datasets covering node classification and link prediction tasks demonstrate superior performance compared to conventional standalone methodologies. The proposed framework accelerates convergence, optimizes feature distribution, and provides a scalable solution for a wide range of graph-based learning paradigms.