Graph Neural Network (GNN), with the main idea of encoding graph structure
information of graphs by propagation and aggregation, has developed rapidly. It
achieved excellent performance in representation learning of multiple types of
graphs such as homogeneous graphs, heterogeneous graphs, and more complex
graphs like knowledge graphs. However, merely stacking GNN layers may not
improve the model's performance and can even be detrimental. For the phenomenon
of performance degradation in deep GNNs, we propose a new perspective. Unlike
the popular explanations of over-smoothing or over-squashing, we think the
issue arises from the interference of low-quality node representations during
message propagation. We introduce a simple and general method, SF-GNN, to
address this problem. In SF-GNN, we define two representations for each node,
one is the node representation that represents the feature of the node itself,
and the other is the message representation specifically for propagating
messages to neighbor nodes. A self-filter module evaluates the quality of the
node representation and decides whether to integrate it into the message
propagation based on this quality assessment. Experiments on node
classification tasks for both homogeneous and heterogeneous graphs, as well as
link prediction tasks on knowledge graphs, demonstrate that our method can be
applied to various GNN models and outperforms state-of-the-art baseline methods
in addressing deep GNN degradation.

图神经网络（GNN）通过传播和聚合的方式对图的结构信息进行编码，取得了显著的表征学习性能，但是仅仅叠加 GNN 层可能不会提高模型的性能，甚至可能有害。针对深层 GNN 出现的性能下降现象，我们提出了一种新的观点。我们认为这个问题不是过度平滑或过度压缩的原因，而是由于消息传递过程中低质量节点表示的干扰。我们引入了一种简单而通用的方法 SF-GNN 来解决这个问题。在 SF-GNN 中，我们为每个节点定义了两个表示，一个是节点本身的特征表示，另一个是专门用于传播消息到邻居节点的消息表示。通过自过滤模块评估节点表示的质量，并根据质量评估决定是否将其整合到消息传播中。对同质和异质图的节点分类任务以及知识图谱的链接预测任务的实验表明，我们的方法可以应用于各种 GNN 模型，并在解决深层 GNN 性能下降方面优于现有方法。