Network representation learning: A macro and micro view

AI Open Pub Date : 2021-01-01 DOI:10.1016/j.aiopen.2021.02.001

Xueyi Liu , Jie Tang

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引用次数: 15

Abstract

Graph is a universe data structure that is widely used to organize data in real-world. Various real-word networks like the transportation network, social and academic network can be represented by graphs. Recent years have witnessed the quick development on representing vertices in the network into a low-dimensional vector space, referred to as network representation learning. Representation learning can facilitate the design of new algorithms on the graph data. In this survey, we conduct a comprehensive review of current literature on network representation learning. Existing algorithms can be categorized into three groups: shallow embedding models, heterogeneous network embedding models, graph neural network based models. We review state-of-the-art algorithms for each category and discuss the essential differences between these algorithms. One advantage of the survey is that we systematically study the underlying theoretical foundations underlying the different categories of algorithms, which offers deep insights for better understanding the development of the network representation learning field.

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网络表示学习:宏观和微观的观点

摘要图是一种广泛用于组织数据的宇宙数据结构。各种现实世界的网络，如交通网络、社会网络和学术网络，都可以用图来表示。近年来，将网络中的顶点表示为低维向量空间的方法得到了迅速发展，称为网络表示学习。表示学习有助于在图数据上设计新的算法。在这项调查中，我们对网络表征学习的当前文献进行了全面的回顾。现有的算法可分为三类:浅嵌入模型、异构网络嵌入模型和基于图神经网络的模型。我们回顾了每个类别的最新算法，并讨论了这些算法之间的本质区别。该调查的一个优点是我们系统地研究了不同类别算法的潜在理论基础，这为更好地理解网络表示学习领域的发展提供了深刻的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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