Graph pooling for graph-level representation learning: a survey

Abstract

In graph-level representation learning tasks, graph neural networks have received much attention for their powerful feature learning capabilities. However, with the increasing scales of graph data, how to efficiently process and extract the key information has become the focus of research. The graph pooling technique, as a key step in graph neural networks, simplifies the graph structure by merging nodes or subgraphs, which significantly improves the computational efficiency and feature extraction ability of graph neural networks. Although various graph pooling methods have been proposed by numerous scholars, there is still a relative lack of systematic summaries of these works. In this paper, we comprehensively sort out the fundamentals and recent progress of graph pooling techniques in graph neural networks and discuss its wide range of application scenarios, as well as the current challenges and opportunities, which point out the direction for future research. Specifically, we first provide a detailed introduction to the basics of graph pooling, including its definition, principles, and its function in graph neural networks. Then, we categorize and summarize the research preliminaries of graph pooling, including various graph pooling methods proposed in recent years. Next, we explore the potential of graph pooling for a wide range of applications, which provides insightful insights for the promotion and practice of graph pooling technology in more fields. Furthermore, we conduct a comparative analysis of various graph pooling methods and evaluate their performance on a benchmark dataset, providing a comprehensive understanding of their strengths and weaknesses. Finally, we systematically analyze the challenges and opportunities of the current graph pooling methods and provide a prospective outlook on future research directions.