Enhancing Graph Representations Learning with Decorrelated Propagation

In recent years, graph neural networks (GNNs) have been widely used in many domains due to their powerful capability in representation learning on graph-structured data. While a majority of extant studies focus on mitigating the over-smoothing problem, recent works also reveal the limitation of GNN from a new over-correlation perspective which states that the learned representation becomes highly correlated after feature transformation and propagation in GNNs. In this paper, we thoroughly re-examine the issue of over-correlation in deep GNNs, both empirically and theoretically. We demonstrate that the propagation operator in GNNs exacerbates the feature correlation. In addition, we discovered through empirical study that existing decorrelation solutions fall short of maintaining a low feature correlation, potentially encoding redundant information. Thus, to more effectively address the over-correlation problem, we propose a decorrelated propagation scheme (DeProp) as a fundamental component to decorrelate the feature learning in GNN models, which achieves feature decorrelation at the propagation step. Comprehensive experiments on multiple real-world datasets demonstrate that DeProp can be easily integrated into prevalent GNNs, leading to significant performance enhancements. Furthermore, we find that it can be used to solve over-smoothing and over-correlation problems simultaneously and significantly outperform state-of-the-art methods on missing feature settings. The code is available at https://github.com/hualiu829/DeProp.