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DiGRAF: Diffeomorphic Graph-Adaptive Activation Function

Authors :
Mantri, Krishna Sri Ipsit
Wang, Xinzhi
Schönlieb, Carola-Bibiane
Ribeiro, Bruno
Bevilacqua, Beatrice
Eliasof, Moshe
Publication Year :
2024

Abstract

In this paper, we propose a novel activation function tailored specifically for graph data in Graph Neural Networks (GNNs). Motivated by the need for graph-adaptive and flexible activation functions, we introduce DiGRAF, leveraging Continuous Piecewise-Affine Based (CPAB) transformations, which we augment with an additional GNN to learn a graph-adaptive diffeomorphic activation function in an end-to-end manner. In addition to its graph-adaptivity and flexibility, DiGRAF also possesses properties that are widely recognized as desirable for activation functions, such as differentiability, boundness within the domain and computational efficiency. We conduct an extensive set of experiments across diverse datasets and tasks, demonstrating a consistent and superior performance of DiGRAF compared to traditional and graph-specific activation functions, highlighting its effectiveness as an activation function for GNNs.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2407.02013
Document Type :
Working Paper