Vector representations of graphs and relational structures, whether hand-crafted feature vectors or learned representations, enable us to apply standard data analysis and machine learning techniques to the structures. A wide range of methods for generating such embeddings have been studied in the machine learning and knowledge representation literature. However, vector embeddings have received relatively little attention from a theoretical point of view. Starting with a survey of embedding techniques that have been used in practice, in this paper we propose two theoretical approaches that we see as central for understanding the foundations of vector embeddings. We draw connections between the various approaches and suggest directions for future research.

中文翻译：

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word2vec、node2vec、graph2vec、X2vec：走向结构化数据向量嵌入理论

图和关系结构的向量表示，无论是手工制作的特征向量还是学习的表示，都使我们能够将标准数据分析和机器学习技术应用于结构。在机器学习和知识表示文献中已经研究了多种用于生成此类嵌入的方法。然而，从理论的角度来看，向量嵌入受到的关注相对较少。从对实践中使用的嵌入技术的调查开始，在本文中，我们提出了两种理论方法，我们认为它们是理解向量嵌入基础的核心。我们在各种方法之间建立联系，并为未来的研究提出建议。