图自然出现在众多应用领域中,从社会分析,生物信息学到计算机视觉。图形的独特功能使得能够捕获数据之间的结构关系,因此,与孤立地分析数据相比,可以获得更多的见解。然而,解决图形上的学习问题通常非常具有挑战性,因为(1)许多类型的数据最初不是图的结构,如图像和文本数据,(2)对于图结构数据,潜在的连接模式通常是复杂和多样的。另一方面,代表性学习在许多领域取得了巨大的成功。因此,一个潜在的解决方案是学习图在低维欧几里得空间中的表示,这样可以保留图形属性。尽管已经做出了巨大的努力来解决图表示学习问题,他们中的许多人仍然受到他们肤浅的学习机制的困扰。图上的深度学习模型(例如,图神经网络)最近出现在机器学习和其他相关领域,并在各种问题上表现出卓越的性能。在这次调查中,尽管有多种类型的图神经网络,我们专门针对图卷积网络的新兴领域进行了全面的回顾,这是最突出的图形深度学习模型之一。首先,我们根据卷积的类型将现有的图卷积网络模型分为两类,并详细介绍了一些图卷积网络模型。然后,我们根据其应用领域对不同的图卷积网络进行分类。最后,我们在这一领域提出了几个开放的挑战,并讨论了未来研究的潜在方向。
Graphs naturally appear in numerous application domains, ranging from social analysis, bioinformatics to computer vision. The unique capability of graphs enables capturing the structural relations among data, and thus allows to harvest more insights compared to analyzing data in isolation. However, it is often very challenging to solve the learning problems on graphs, because (1) many types of data are not originally structured as graphs, such as images and text data, and (2) for graph-structured data, the underlying connectivity patterns are often complex and diverse. On the other hand, the representation learning has achieved great successes in many areas. Thereby, a potential solution is to learn the representation of graphs in a low-dimensional Euclidean space, such that the graph properties can be preserved. Although tremendous efforts have been made to address the graph representation learning problem, many of them still suffer from their shallow learning mechanisms. Deep learning models on graphs (e.g., graph neural networks) have recently emerged in machine learning and other related areas, and demonstrated the superior performance in various problems. In this survey, despite numerous types of graph neural networks, we conduct a comprehensive
review specifically on the emerging field of graph convolutional networks, which is one of the most prominent graph deep learning models. First, we group the existing graph convolutional network models into two categories based on the types of convolutions and highlight some graph convolutional network models in details. Then, we categorize different graph convolutional networks according to the areas of their applications. Finally, we present several open challenges in this area and discuss potential directions for future research.