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 four categories, namely recurrent graph neural
networks, convolutional graph neural networks, graph autoencoders,
and spatial-temporal graph neural networks. We further
discuss the applications of graph neural networks across various
domains and summarize the open source codes, benchmark data
sets, and model evaluation of graph neural networks. Finally,
we propose potential research directions in this rapidly growing
field.
