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:图表在性质上是无处不在的,因此可以作为许多实际问题和理论问题的模型。为此目的,它们可以被定义为许多不同类型,适当反映所代表问题的具体背景。为了解决基于图表数据的尖端问题,已经出现了图形神经网络(GNNs)的研究领域。尽管实地的年轻程度和开发新模型的速度,许多最近的调查已经公布,以跟踪这些模型。然而,还没有收集到GNN能够处理何种类型的图表。在本次调查中,我们详细概述了现有的GNNs,与以往的调查不同,我们根据它们处理不同图形类型和属性的能力对它们进行分类。我们考虑到GNNS使用不同结构结构的静态和动态图形,有的或无节点或边缘属性。此外,我们区分了离散时间或连续时间动态图形的GNNM模型,并根据模型的结构对模型加以分组。我们发现,仍然有一些没有或很少被现有的GNNM模型覆盖的图形类型。我们指出,并给出了缺少模型的潜在原因。