Graph-structured data 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, graph mining is a challenging task due to the underlying complex and diverse connectivity patterns. A potential solution is to learn the representation of a graph in a low-dimensional Euclidean space via embedding techniques that preserve the graph properties. Although tremendous efforts have been made to address the graph representation learning problem, many of them still suffer from their shallow learning mechanisms. On the other hand, deep learning models on graphs have recently emerged in both machine learning and data mining areas and demonstrated superior performance for various problems. In this survey, 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. We first introduce two taxonomies to group the existing works based on the types of convolutions and the areas of applications, then highlight some graph convolutional network models in details. Finally, we present several challenges in this area and discuss potential directions for future research.

Original languageEnglish (US)
Title of host publicationComputational Data and Social Networks - 7th International Conference, CSoNet 2018, Proceedings
EditorsMy T. Thai, Xuemin Chen, Wei Wayne Li, Arunabha Sen
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783030046477
StatePublished - 2018
Event7th International Conference on Computational Data and Social Networks, CSoNet 2018 - Shanghai, China
Duration: Dec 18 2018Dec 20 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11280 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other7th International Conference on Computational Data and Social Networks, CSoNet 2018


  • Graph convolutional networks
  • Spatial
  • Spectral

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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