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A Comprehensive Survey on Graph Neural Networks

机译：图形神经网络综合调查

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摘要

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications, where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on the existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this article, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art GNNs into four categories, namely, recurrent GNNs, convolutional GNNs, graph autoencoders, and spatial–temporal GNNs. We further discuss the applications of GNNs across various domains and summarize the open-source codes, benchmark data sets, and model evaluation of GNNs. Finally, we propose potential research directions in this rapidly growing field.
机译：深入学习近年来彻底改变了许多机器学习任务，从图像分类和视频处理到语音识别和自然语言理解。这些任务中的数据通常表示在欧几里德空间中。然而，存在越来越多的应用程序，其中数据来自非欧几里德域生成，并且被表示为具有复杂关系和对象之间的相互依赖性的图表。图表数据的复杂性对现有机器学习算法造成了重大挑战。最近，许多关于扩展图形数据深入学习方法的研究。在本文中，我们在数据挖掘和机器学习领域中提供了图形神经网络（GNNS）的全面概述。我们提出了一种新的分类法将最先进的GNN分为四类，即经常性GNN，卷积GNN，图形AutoEncoders和空间时间GNN。我们进一步讨论了GNNS在各个域中的应用程序，并总结了GNN的开源代码，基准数据集和模型评估。最后，我们提出了在这种快速生长的领域中的潜在研究方向。

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