Graphs, such as social networks, word co-occurrence networks, and communication networks, occur naturally in various real-world applications. Analyzing them yields insight into the structure of society, language, and different patterns of communication. Many approaches have been proposed to perform the analysis. Recently, methods which use the representation of graph nodes in vector space have gained traction from the research community. In this survey, we provide a comprehensive and structured analysis of various graph embedding techniques proposed in the literature. We first introduce the embedding task and its challenges such as scalability, choice of dimensionality, and features to be preserved, and their possible solutions. We then present three categories of approaches based on factorization methods, random walks, and deep learning, with examples of representative algorithms in each category and analysis of their performance on various tasks. We evaluate these state-of-the-art methods on a few common datasets and compare their performance against one another and versus non-embedding based models. Our analysis concludes by suggesting some potential applications and future directions. We finally present the open-source Python library, named GEM (Graph Embedding Methods), we developed that provides all presented algorithms within a unified interface, to foster and facilitate research on the topic.

本文综述了图嵌入技术的各种方法，介绍了三个不同的分类方法的代表性算法，并分析了它们在不同任务上的表现。文章最终结论是提出了一些潜在的应用和未来方向，并介绍了一个名为GEM的Python库，其中提供了所有介绍的算法作为一个统一的接口，以促进和便利这个领域的研究。

图嵌入技术、应用及性能：一项调查