  Recent years have witnessed remarkable success achieved by graph neural
networks (GNNs) in many real-world applications such as recommendation and drug
discovery. Despite the success, oversmoothing has been identified as one of the
key issues which limit the performance of deep GNNs. It indicates that the
learned node representations are highly indistinguishable due to the stacked
aggregators. In this paper, we propose a new perspective to look at the
performance degradation of deep GNNs, i.e., feature overcorrelation. Through
empirical and theoretical study on this matter, we demonstrate the existence of
feature overcorrelation in deeper GNNs and reveal potential reasons leading to
this issue. To reduce the feature correlation, we propose a general framework
DeCorr which can encourage GNNs to encode less redundant information. Extensive
experiments have demonstrated that DeCorr can help enable deeper GNNs and is
complementary to existing techniques tackling the oversmoothing issue.
