Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. Using the constructed feature spaces, many machine learning problems on graphs can be solved via standard frameworks suitable for vectorized feature representation. Our survey aims to describe the core concepts of graph embeddings and provide several taxonomies for their description. First, we start with the methodological approach and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. Next, we describe how different types of networks impact the ability of models to incorporate structural and attributed data into a unified embedding. Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classification, similarity and alignment problems. Finally, we overview the existing applications of graph embeddings to computer science domains, formulate open problems and provide experiment results, explaining how different networks properties result in graph embeddings quality in the four classic machine learning problems on graphs, such as node classification, link prediction, clustering and graph visualization. As a result, our survey covers a new rapidly growing field of network feature engineering, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs.

中文翻译：

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图嵌入研究及其在图上机器学习问题中的应用

处理关系数据始终需要大量的计算资源，领域专业知识和与任务相关的特征工程，才能将结构信息整合到预测模型中。如今，在不同的文献流中已经提出了一系列自动图形特征工程技术。所谓的图嵌入提供了一个强大的工具，可以在保留内部图属性的情况下为图及其组件（例如节点，边和子图）构造矢量化特征空间。使用构造的特征空间，可以通过适用于矢量化特征表示的标准框架解决图形上的许多机器学习问题。我们的调查旨在描述图嵌入的核心概念，并提供几种分类法进行描述。第一，我们从方法论方法入手，并基于矩阵分解，随机游走和深度学习方法提取三种类型的图嵌入模型。接下来，我们描述不同类型的网络如何影响模型将结构化数据和属性数据合并到统一嵌入中的能力。更进一步，我们对图嵌入应用程序进行了全面的评估，以解决图上的机器学习问题，其中包括节点分类，链接预测，聚类，可视化，压缩，以及适用于图分类，相似性和相似性的整个图嵌入算法系列。对齐问题。最后，我们概述了图形嵌入在计算机科学领域的现有应用，提出了开放性问题并提供了实验结果，解释了不同的网络属性如何导致图上的四个经典机器学习问题中的图嵌入质量，例如节点分类，链接预测，聚类和图可视化。因此，我们的调查涵盖了快速发展的网络功能工程领域，提供了基于网络类型的模型的深入分析，并概述了图上机器学习问题的广泛应用。