Graphs have a superior ability to represent relational data, like chemical compounds, proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as input, has been applied to many tasks including comparison, regression, classification, and more. Traditional approaches to learning a set of graphs heavily rely on hand-crafted features, such as substructures. While these methods benefit from good interpretability, they often suffer from computational bottlenecks as they cannot skirt the graph isomorphism problem. Conversely, deep learning has helped graph-level learning adapt to the growing scale of graphs by extracting features automatically and encoding graphs into low-dimensional representations. As a result, these deep graph learning methods have been responsible for many successes. Yet, no comprehensive survey reviews graph-level learning starting with traditional learning and moving through to the deep learning approaches. This article fills this gap and frames the representative algorithms into a systematic taxonomy covering traditional learning, graph-level deep neural networks, graph-level graph neural networks, and graph pooling. In addition, the evolution and interaction between methods from these four branches within their developments are examined to provide an in-depth analysis. This is followed by a brief review of the benchmark datasets, evaluation metrics, and common downstream applications. Finally, the survey concludes with an in-depth discussion of 12 current and future directions in this booming field.

中文翻译：

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图级学习的最新技术和潜力


图形具有表示关系数据（如化合物、蛋白质和社交网络）的卓越能力。因此，将一组图形作为输入的图级学习已应用于许多任务，包括比较、回归、分类等。学习一组图形的传统方法严重依赖手工制作的特征，例如子结构。虽然这些方法受益于良好的可解释性，但它们经常受到计算瓶颈的影响，因为它们无法绕过图同构问题。相反，深度学习通过自动提取特征并将图形编码为低维表示形式，帮助图形级学习适应不断增长的图形规模。因此，这些深度图学习方法取得了许多成功。然而，没有全面的调查回顾了从传统学习开始到深度学习方法的图级学习。本文填补了这一空白，并将代表性算法构建成一个系统分类法，涵盖传统学习、图级深度神经网络、图级图神经网络和图池化。此外，还研究了这四个分支在其发展过程中方法的演变和相互作用，以提供深入分析。然后简要回顾基准数据集、评估指标和常见的下游应用程序。最后，调查以深入讨论这个蓬勃发展的领域的 12 个当前和未来方向结束。