Distinguishing Neighborhood Representations Through Reverse Process of
  GNNs for Heterophilic Graphs
MoonJeong Park Jaeseung Heo Dongwoo Kim
Graph Neural Network (GNN) resembles the diffusion process, leading to the
over-smoothing of learned representations when stacking many layers. Hence, the
reverse process of message passing can sharpen the node representations by
inverting the forward message propagation. The sharpened representations can
help us to better distinguish neighboring nodes with different labels, such as
in heterophilic graphs. In this work, we apply the design principle of the
reverse process to the three variants of the GNNs. Through the experiments on
heterophilic graph data, where adjacent nodes need to have different
representations for successful classification, we show that the reverse process
significantly improves the prediction performance in many cases. Additional
analysis reveals that the reverse mechanism can mitigate the over-smoothing
over hundreds of layers.