Abstract
Graph Neural Networks (GNNs) have demonstrated remarkable performance on various graph-related tasks. However, their effectiveness often diminishes when applied to heterophilic graphs, where interconnected nodes exhibit dissimilar attributes. This paper presents a novel approach, Adaptive Feature Transformation (AFT), designed to mitigate the challenges posed by heterophily. AFT incorporates a dynamic feature transformation mechanism, allowing nodes to adaptively adjust their representations based on the properties of their neighbors. We evaluate AFT on several benchmark heterophilic datasets and demonstrate that it achieves substantial performance gains over existing state-of-the-art GNN models. Our results underscore the importance of adaptive feature learning for processing complex, heterophilic network structures.
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This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
"Addressing Challenges in Graph Neural Networks Through Adaptive Feature Transformation", Technical Disclosure Commons, (January 07, 2025)
https://www.tdcommons.org/dpubs_series/7709