In many ways, graphs are the main modality of data we receive from nature. This is due to the fact that most of the patterns we see, both in natural and artificial systems, are elegantly representable using the language of graph structures. Prominent examples include molecules (represented as graphs of atoms and bonds), social networks and transportation networks. This potential has already been seen by key scientific and industrial groups, with already-impacted application areas including traffic forecasting, drug discovery, social network analysis and recommender systems. Further, some of the most successful domains of application for machine learning in previous years -- images, text and speech processing -- can be seen as special cases of graph representation learning, and consequently there has been significant exchange of information between these areas. The main aim of this short survey is to enable the reader to assimilate the key concepts in the area, and position graph representation learning in a proper context with related fields.

翻译：在许多方面,图表是我们从自然界获得数据的主要模式,这是因为在自然和人工系统中,我们看到的多数模式,都使用图形结构的语言,以优雅的方式呈现,突出的例子包括分子(以原子和债券图示表示)、社交网络和运输网络,主要科学和工业团体已经看到这种潜力,包括交通预测、毒品发现、社会网络分析和推荐系统等已经受到影响的应用领域。此外,前几年最成功的机器学习应用领域,如图像、文本和语音处理,可以视为图表教学的特殊案例,因此这些领域之间已进行了大量信息交流。这次短期调查的主要目的是使读者能够将该领域的关键概念同化,并将图表教学与相关领域放在适当的背景下。