A Comprehensive Review of the Oversmoothing in Graph Neural Networks

There are many ways to process graph data in deep learning, among which Graph Neural Network(GNN) is an effective and popular deep learning model. However, GNN also has some problems. For example, after multiple layers of neural networks, the features between nodes will become more and more similar, so that the model identifies two completely different nodes as one type. For example, when two nodes with different structural information output, they are almost the same at the feature level and thus difficult to be distinguished, and this phenomenon is called oversmoothing. For example, in node classification, two completely different types of nodes obtain highly similar node features after model training. How to alleviate and solve the oversmoothing problem has become an emerging hot research topic in graph research. However, there has yet to be an extensive investigation and evaluation of this topic. This paper aims to summarize different approaches to mitigate the oversmoothing phenomenon by providing a detailed research survey. We analyze and summarize proposed research schemes from three aspects currently: topological perturbation, message passing, and adaptive learning, and evaluate the strengths and limitations of existing research by outlining oversmoothing evaluation methods. In addition, we predict and summarize promising and possible research paths in the future. In doing so, this paper contributes to the development of GNN and provides insightful information for practitioners working with GNN and graph data.