As large-scale graphs become more widespread, more and more computational challenges with extracting, processing, and interpreting large graph data are being exposed. It is therefore natural to search for ways to summarize these expansive graphs while preserving their key characteristics. In the past, most graph summarization techniques sought to capture the most important part of a graph statistically. However, today, the high dimensionality and complexity of modern graph data are making deep learning techniques more popular. Hence, this paper presents a comprehensive survey of progress in deep learning summarization techniques that rely on graph neural networks (GNNs). Our investigation includes a review of the current state-of-the-art approaches, including recurrent GNNs, convolutional GNNs, graph autoencoders, and graph attention networks. A new burgeoning line of research is also discussed where graph reinforcement learning is being used to evaluate and improve the quality of graph summaries. Additionally, the survey provides details of benchmark datasets, evaluation metrics, and open-source tools that are often employed in experimentation settings, along with a discussion on the practical uses of graph summarization in different fields. Finally, the survey concludes with a number of open research challenges to motivate further study in this area.

翻译：随着大规模图数据的日益普及，提取、处理及解释大型图数据所带来的计算挑战愈发凸显。因此，寻找既能压缩海量图结构又能保留其关键特征的方法成为自然需求。以往，多数图摘要技术主要依赖统计方法捕获图中最重要的部分。然而，当今现代图数据的高维度与复杂性使得深度学习技术更受青睐。为此，本文全面综述了基于图神经网络（GNNs）的深度学习摘要技术的研究进展。我们系统梳理了当前最前沿的方法，包括递归图神经网络、卷积图神经网络、图自编码器以及图注意力网络。同时，探讨了新兴研究方向——利用图强化学习评估并提升图摘要质量。此外，本文详细介绍了实验场景中常用的基准数据集、评估指标与开源工具，并讨论了图摘要在不同领域的实际应用。最后，我们总结了若干开放研究挑战，以推动该领域的进一步探索。