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 detailed comparison, discussion, and takeaways for the research community focused on graph summarization. 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、处理和解释大型图数据所面临的计算挑战越来越多。因此，寻求在保留图数据关键特征的同时对其进行摘要的方法变得理所当然。过去，大多数图摘要技术试图以统计方式捕捉图的最重要部分。然而，如今现代图数据的高维度和复杂性使得深度学习技术越来越受欢迎。因此，本文全面综述了依赖于图神经网络（GNN）的深度学习摘要技术的进展。我们的研究涵盖了当前最先进方法的回顾，包括循环图神经网络（Recurrent GNNs）、卷积图神经网络（Convolutional GNNs）、图自编码器（Graph Autoencoders）和图注意力网络（Graph Attention Networks）。此外，本文还讨论了一个新兴的研究方向——利用图强化学习（Graph Reinforcement Learning）来评估和改进图摘要的质量。同时，本综述提供了实验环境中常用的基准数据集、评估指标和开源工具的详细信息，并对图摘要研究社区进行了详细的比较、讨论和启示。最后，本综述提出了一些开放性的研究挑战，以激励该领域的进一步研究。