A Comprehensive Survey on Graph Neural Networks
A Comprehensive Survey on Graph Neural Networks
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DOI：
10.1109/tnnls.2020.2978386
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发表时间：
2021-01-01
影响因子：
10.4
通讯作者：
Yu, Philip S.
中科院分区：
文献类型：
--
作者：
Wu, Zonghan;Pan, Shirui;Yu, Philip S.
近年来，深度学习给许多机器学习任务带来了革命性的变化，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以欧几里得空间表示。然而，有越来越多的应用程序，其中数据是从非欧几里德领域产生的，并被表示为具有复杂关系和对象之间相互依赖的图。图数据的复杂性给现有的机器学习算法带来了巨大的挑战。近年来，关于扩展图形数据深度学习方法的研究层出不穷。在本文中，我们对数据挖掘和机器学习领域中的图神经网络(GNN)进行了全面的概述。我们提出了一种新的分类方法，将最新的GNN分为四类，即循环GNN、卷积GNN、图自动编码器和时空GNN。我们进一步讨论了GNN在各个领域的应用，并总结了GNN的开源代码、基准数据集和模型评估。最后，我们提出了这一快速发展领域的潜在研究方向。
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.