Effective gene network representation learning is of great importance in
bioinformatics to predict/understand the relation of gene profiles and disease
phenotypes. Though graph neural networks (GNNs) have been the dominant
architecture for analyzing various graph-structured data like social networks,
their predicting on gene networks often exhibits subpar performance. In this
paper, we formally investigate the gene network representation learning problem
and characterize a notion of \textit{universal graph normalization}, where
graph normalization can be applied in an universal manner to maximize the
expressive power of GNNs while maintaining the stability. We propose a novel
UNGNN (Universal Normalized GNN) framework, which leverages universal graph
normalization in both the message passing phase and readout layer to enhance
the performance of a base GNN. UNGNN has a plug-and-play property and can be
combined with any GNN backbone in practice. A comprehensive set of experiments
on gene-network-based bioinformatical tasks demonstrates that our UNGNN model
significantly outperforms popular GNN benchmarks and provides an overall
performance improvement of 16 $\%$ on average compared to previous
state-of-the-art (SOTA) baselines. Furthermore, we also evaluate our
theoretical findings on other graph datasets where the universal graph
normalization is solvable, and we observe that UNGNN consistently achieves the
superior performance.

基于基因网络的表示学习中，我们提出了一种新颖的 UNGNN (Universal Normalized GNN) 框架，它通过在消息传递阶段和读取层中利用通用图归一化来增强基于 GNN 的性能，实现了 16% 的整体性能提升。