A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications
A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications
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DOI：
10.1109/tkde.2018.2807452
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发表时间：
2018-09-01
影响因子：
8.9
通讯作者：
Chang, Kevin Chen-Chuan
中科院分区：
文献类型：
--
作者：
Cai, HongYun;Zheng, Vincent W.;Chang, Kevin Chen-Chuan
图是一种重要的数据表示形式，出现在各种现实场景中。有效的图分析为用户提供了更深入的了解数据背后的内容，从而可以使许多有用的应用程序受益，例如节点分类，节点推荐，链接预测等。图嵌入是解决图分析问题的一种有效而高效的方法。它将图数据转换到一个最大限度地保留了图的结构信息和属性的低维空间。在这次调查中，我们对图嵌入的文献进行了全面的回顾。首先介绍了图嵌入的形式化定义以及相关概念。在此之后，我们提出了图嵌入的两种分类法，它们对应于不同图嵌入问题设置中存在的挑战以及现有工作如何在解决方案中解决这些挑战。最后，我们总结了图嵌入的应用，并在计算效率，问题设置，技术和应用场景方面提出了四个有前途的未来研究方向。
Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behind the data, and thus can benefit a lot of useful applications such as node classification, node recommendation, link prediction, etc. However, most graph analytics methods suffer the high computation and space cost. Graph embedding is an effective yet efficient way to solve the graph analytics problem. It converts the graph data into a low dimensional space in which the graph structural information and graph properties are maximumly preserved. In this survey, we conduct a comprehensive review of the literature in graph embedding. We first introduce the formal definition of graph embedding as well as the related concepts. After that, we propose two taxonomies of graph embedding which correspond to what challenges exist in different graph embedding problem settings and how the existing work addresses these challenges in their solutions. Finally, we summarize the applications that graph embedding enables and suggest four promising future research directions in terms of computation efficiency, problem settings, techniques, and application scenarios.