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About this paper
Arman Gupta, Govind Waghmare, Gaurav Oberoi, Nitish Srivastava
This work introduces Poly and Dir-Poly, two graph neural network architectures that enhance expressivity through polynomial transformations and explicitly incorporate edge directionality. The models achieve strong performance on several heterophilic datasets; Dir-Poly obtained 94.51% accuracy on Roman Empire, and Poly achieved 95.32 ROC AUC on Minesweeper, demonstrating the benefits of combining these features for robust graph analysis.
Key insights
- Polynomial transformations significantly enhance the representational power of GNNs, enabling them to model complex relationships more effectively in heterophilic graphs.
- Explicitly modeling edge directionality provides crucial signals that can substantially improve GNN performance, especially when the graph's inherent structure contains meaningful directed relationships.
- The combined effect of polynomial expressivity and edge directionality offers a synergistic approach to developing more powerful and adaptable GNNs for challenging heterophilic graph structures.
Summary by alphaXiv.
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