Graph Neural Networks (GNNs) are powerful tools for graph representation learning. Despite their rapid development, GNNs also face some challenges, such as over-fitting, over-smoothing, and non-robustness. Previous works indicate that these problems can be alleviated by random dropping methods, which integrate augmented data into models by randomly masking parts of the input. However, some open problems of random dropping on GNNs remain to be solved. First, it is challenging to find a universal method that are suitable for all cases considering the divergence of different datasets and models. Second, augmented data introduced to GNNs causes the incomplete coverage of parameters and unstable training process. Third, there is no theoretical analysis on the effectiveness of random dropping methods on GNNs. In this paper, we propose a novel random dropping method called DropMessage, which performs dropping operations directly on the propagated messages during the message-passing process. More importantly, we find that DropMessage provides a unified framework for most existing random dropping methods, based on which we give theoretical analysis of their effectiveness. Furthermore, we elaborate the superiority of DropMessage: it stabilizes the training process by reducing sample variance; it keeps information diversity from the perspective of information theory, enabling it become a theoretical upper bound of other methods. To evaluate our proposed method, we conduct experiments that aims for multiple tasks on five public datasets and two industrial datasets with various backbone models. The experimental results show that DropMessage has the advantages of both effectiveness and generalization, and can significantly alleviate the problems mentioned above.

翻译：图神经网络（GNNs）是图表示学习的强大工具。尽管发展迅速，图神经网络仍面临过拟合、过平滑和鲁棒性不足等挑战。已有研究表明，通过随机丢弃方法可以缓解这些问题——该方法通过随机遮蔽部分输入为模型引入增强数据。然而，图神经网络中随机丢弃方法仍存在若干待解决问题：首先，鉴于不同数据集和模型的差异性，难以找到适用于所有情况的通用方法；其次，引入增强数据会导致参数覆盖不完整和训练过程不稳定；最后，缺乏关于随机丢弃方法对图神经网络有效性的理论分析。本文提出一种名为DropMessage的新型随机丢弃方法，该方法直接在消息传递过程中对传播的消息执行丢弃操作。更重要的是，我们发现DropMessage为现有大多数随机丢弃方法提供了统一框架，并基于此对其有效性进行了理论分析。此外，我们阐述了DropMessage的优越性：通过降低样本方差稳定训练过程；从信息论角度保持信息多样性，使其成为其他方法的理论上限。为评估所提方法，我们在五个公开数据集和两个工业数据集上使用多种主干模型进行多任务实验。实验结果表明，DropMessage兼具有效性和泛化性，能显著缓解上述问题。