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.

该论文提出了一种针对图数据在图神经网络（GNN）中的新型激活函数。通过引入DiGRAF（基于连续分段仿射变换的图自适应的可微分激活函数），结合额外的GNN以端到端的方式学习图自适应的微分同胚激活函数，实现激活函数的图自适应性和灵活性。通过在多个数据集和任务上进行广泛的实验，证明了DiGRAF与传统和图特定激活函数相比具有一致且卓越的性能，凸显其作为GNN的激活函数的效果。

DiGRAF: 仿射形变图自适应激活函数