Abstract
Graph Neural Networks (GNNs) have shown impressive results in analyzing graph-structured data. However, their performance is often compromised when applied to heterophilous graphs, where connected nodes tend to have dissimilar features. This paper introduces the Structure-Aware Graph Neural Network (SAGNN), a novel GNN architecture designed to handle heterophilous graphs by explicitly encoding and leveraging structural information. SAGNN uses a structure encoding module to generate structural node features, followed by an adaptive message aggregation mechanism based on structural similarity, and finally a structure-aware combination module to generate final embeddings. We provide the algorithm of SAGNN and also a Python implementation with PyTorch. Extensive experimental results on benchmark heterophilous graph datasets demonstrate that SAGNN significantly outperforms both standard GNN models and existing state-of-the-art heterophilyfocused methods.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
"Structure-Aware Graph Neural Network for Node Classification", Technical Disclosure Commons, (March 10, 2025)
https://www.tdcommons.org/dpubs_series/7884