Reducing Oversmoothing in Graph Neural Networks by Changing the Activation Function

Dimitrios Kelesis, Dimitrios Vogiatzis, Georgios Katsimpras, Dimitris Fotakis, Georgios Paliouras

Abstract: The performance of Graph Neural Networks (GNNs) deteriorates as the depth of the network increases. That performance drop is mainly attributed to oversmoothing, which leads to similar node representations through repeated graph convolutions. We show that in deep GNNs the activation function plays a crucial role in oversmoothing. We explain theoretically why this is the case and propose a simple modification to the slope of ReLU to reduce oversmoothing. The proposed approach enables deep networks without the need to change the network architecture or to add residual connections. We verify the theoretical results experimentally and further show that deep networks, which do not suffer from oversmoothing, are beneficial in the presence of the “cold start” problem, i.e. when there is no feature information about unlabeled nodes.