Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. For this purpose, they can be defined as many different types which suitably reflect the individual contexts of the represented problem. To address cutting-edge problems based on graph data, the research field of Graph Neural Networks (GNNs) has emerged. Despite the field's youth and the speed at which new models are developed, many recent surveys have been published to keep track of them. Nevertheless, it has not yet been gathered which GNN can process what kind of graph types. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static and dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover, we distinguish between GNN models for discrete-time or continuous-time dynamic graphs and group the models according to their architecture. We find that there are still graph types that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.

翻译：图在自然界中无处不在，因此可以作为许多实际及理论问题的模型。为此，图可被定义为多种类型，以恰当反映所表示问题的具体背景。为了处理基于图数据的前沿问题，图神经网络（GNN）研究领域应运而生。尽管该领域发展迅速且新模型层出不穷，但已有许多近期综述致力于跟踪这些进展。然而，目前尚未系统梳理哪些GNN能够处理何种图类型。本文对现有GNN进行了详细综述，与以往研究不同，我们根据其处理不同图类型及属性的能力进行分类。我们考察了适用于不同结构构成的静态图与动态图的GNN，包括是否包含节点或边属性。此外，我们区分了面向离散时间或连续时间动态图的GNN模型，并依据其架构对模型进行分组。研究发现，仍存在某些图类型尚未被或极少被现有GNN模型覆盖。我们指出了缺失模型的领域，并分析了其缺失的潜在原因。