JOURNAL OF L
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T
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X CLASS FILES, VOL. X, NO. X, DECEMBER 2018 1
A Comprehensive Survey on Graph Neural
Networks
Zonghan Wu, Shirui Pan, Member, IEEE, Fengwen Chen, Guodong Long,
Chengqi Zhang, Senior Member, IEEE, Philip S. Yu, Fellow, IEEE
Abstract—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 existing machine learning algorithms. Recently, many studies on extending deep learning
approaches for graph data have emerged. In this survey, 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 graph neural networks into different
categories. With a focus on graph convolutional networks, we review alternative architectures that have recently been developed; these
learning paradigms include graph attention networks, graph autoencoders, graph generative networks, and graph spatial-temporal
networks. We further discuss the applications of graph neural networks across various domains and summarize the open source codes
and benchmarks of the existing algorithms on different learning tasks. Finally, we propose potential research directions in this
fast-growing field.
Index Terms—Deep Learning, graph neural networks, graph convolutional networks, graph representation learning, graph
autoencoder, network embedding
F
1 INTRODUCTION
T
HE recent success of neural networks has boosted re-
search on pattern recognition and data mining. Many
machine learning tasks such as object detection [1], [2], ma-
chine translation [3], [4], and speech recognition [5], which
once heavily relied on handcrafted feature engineering to
extract informative feature sets, has recently been revolu-
tionized by various end-to-end deep learning paradigms,
i.e., convolutional neural networks (CNNs) [6], long short-
term memory (LSTM) [7], and autoencoders. The success
of deep learning in many domains is partially attributed to
the rapidly developing computational resources (e.g., GPU)
and the availability of large training data, and is partially
due to the effectiveness of deep learning to extract latent
representation from Euclidean data (e.g., images, text, and
video). Taking image analysis as an example, an image can
be represented as a regular grid in the Euclidean space.
A convolutional neural network (CNN) is able to exploit
the shift-invariance, local connectivity, and compositionality
of image data [8], and as a result, CNN can extract local
• Z. Wu, F. Chen, G. Long, C. Zhang are with Centre for
Artificial Intelligence, FEIT, University of Technology Sydney,
NSW 2007, Australia (E-mail: zonghan.wu-3@student.uts.edu.au;
fengwen.chen@student.uts.edu.au; guodong.long@uts.edu.au;
chengqi.zhang@uts.edu.au).
• S. Pan is with Faculty of Information Technology, Monash University,
Clayton, VIC 3800, Australia (Email: shirui.pan@monash.edu).
• P. S. Yu is with Department of Computer Science, University of Illinois
at Chicago, Chicago, IL 60607-7053, USA (Email: psyu@uic.edu)
• Corresponding author: Shirui Pan.
Manuscript received Dec xx, 2018; revised Dec xx, 201x.
meaningful features that are shared with the entire datasets
for various image analysis tasks.
While deep learning has achieved great success on Eu-
clidean data, there is an increasing number of applications
where data are generated from the non-Euclidean domain
and need to be effectively analyzed. For instance, in e-
commerce, a graph-based learning system is able to exploit
the interactions between users and products [9], [10], [11]
to make highly accurate recommendations. In chemistry,
molecules are modeled as graphs and their bioactivity needs
to be identified for drug discovery [12], [13]. In a citation
network, papers are linked to each other via citations and
they need to be categorized into different groups [14],
[15]. The complexity of graph data has imposed significant
challenges on existing machine learning algorithms. This is
because graph data are irregular. Each graph has a variable
size of unordered nodes and each node in a graph has
a different number of neighbors, causing some important
operations (e.g., convolutions), which are easy to compute
in the image domain but are not directly applicable to the
graph domain anymore. Furthermore, a core assumption of
existing machine learning algorithms is that instances are
independent of each other. However, this is not the case for
graph data where each instance (node) is related to others
(neighbors) via some complex linkage information, which is
used to capture the interdependence among data, including
citations, friendship, and interactions.
Recently, there is increasing interest in extending deep
learning approaches for graph data. Driven by the success
of deep learning, researchers have borrowed ideas from
convolution networks, recurrent networks, and deep auto-
encoders to design the architecture of graph neural net-
arXiv:1901.00596v2 [cs.LG] 10 Mar 2019
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