In this work, we generalize the ideas of Kaiming initialization to Graph Neural Networks (GNNs) and propose a new scheme (G-Init) that reduces oversmoothing, leading to very good results in node and graph classification tasks. GNNs are commonly initialized using methods designed for other types of Neural Networks, overlooking the underlying graph topology. We analyze theoretically the variance of signals flowing forward and gradients flowing backward in the class of convolutional GNNs. We then simplify our analysis to the case of the GCN and propose a new initialization method. Our results indicate that the new method (G-Init) reduces oversmoothing in deep GNNs, facilitating their effective use. Experimental validation supports our theoretical findings, demonstrating the advantages of deep networks in scenarios with no feature information for unlabeled nodes (i.e., ``cold start'' scenario).

本研究解决了图神经网络（GNNs）在初始化方法上存在的不足，提出了一种新的初始化方案（G-Init），有效减少了过平滑现象。我们的理论分析和实验结果表明，该方法在节点和图分类任务中表现出色，尤其在缺乏特征信息的“冷启动”场景中显示出深度网络的优势。

通过信息化权重初始化减少图神经网络的过平滑