1 research outputs found
ResNorm: Tackling Long-tailed Degree Distribution Issue in Graph Neural Networks via Normalization
Graph Neural Networks (GNNs) have attracted much attention due to their
ability in learning representations from graph-structured data. Despite the
successful applications of GNNs in many domains, the optimization of GNNs is
less well studied, and the performance on node classification heavily suffers
from the long-tailed node degree distribution. This paper focuses on improving
the performance of GNNs via normalization.
In detail, by studying the long-tailed distribution of node degrees in the
graph, we propose a novel normalization method for GNNs, which is termed
ResNorm (\textbf{Res}haping the long-tailed distribution into a normal-like
distribution via \textbf{norm}alization). The operation of ResNorm
reshapes the node-wise standard deviation (NStd) distribution so as to improve
the accuracy of tail nodes (\textit{i}.\textit{e}., low-degree nodes). We
provide a theoretical interpretation and empirical evidence for understanding
the mechanism of the above . In addition to the long-tailed distribution
issue, over-smoothing is also a fundamental issue plaguing the community. To
this end, we analyze the behavior of the standard shift and prove that the
standard shift serves as a preconditioner on the weight matrix, increasing the
risk of over-smoothing. With the over-smoothing issue in mind, we design a
operation for ResNorm that simulates the degree-specific parameter
strategy in a low-cost manner. Extensive experiments have validated the
effectiveness of ResNorm on several node classification benchmark datasets