Novel dropout approach for mitigating over-smoothing in graph neural networks

Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing structured data represented as graphs. They offer significant contributions across various domains due to their ability to effectively capture and process complex relational information. However, most existing GNNs still suffer from undesirable phenomena such as non-robustness, overfitting, and over-smoothing. These challenges have raised significant interest among researchers. In this context, this work aims to address these issues by proposing a new vision of Dropout named A-DropEdge. First, it applies a message-passing layer to ensure the connection between nodes and avoid dropping in the input. Then, the information propagates through many branches with different random configurations to enhance the aggregation process. Moreover, consistency regularization is adopted to perform self-supervised learning. The experimental results on three graph data sets including Cora, Citeseer, and PubMed show the robustness and performance of the proposed approach in mitigating the over-smoothing problem.