  We present Gradient Gating (G$^2$), a novel framework for improving the
performance of Graph Neural Networks (GNNs). Our framework is based on gating
the output of GNN layers with a mechanism for multi-rate flow of message
passing information across nodes of the underlying graph. Local gradients are
harnessed to further modulate message passing updates. Our framework flexibly
allows one to use any basic GNN layer as a wrapper around which the multi-rate
gradient gating mechanism is built. We rigorously prove that G$^2$ alleviates
the oversmoothing problem and allows the design of deep GNNs. Empirical results
are presented to demonstrate that the proposed framework achieves
state-of-the-art performance on a variety of graph learning tasks, including on
large-scale heterophilic graphs.
