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. 2019;6(1):11.
doi: 10.1186/s40649-019-0069-y. Epub 2019 Nov 10.

Graph convolutional networks: a comprehensive review

Si Zhang  1 , Hanghang Tong  1 , Jiejun Xu  2 , Ross Maciejewski  3
Affiliations

Graph convolutional networks: a comprehensive review

Si Zhang et al. Comput Soc Netw. 2019.

Abstract

Graphs 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, it is often very challenging to solve the learning problems on graphs, because (1) many types of data are not originally structured as graphs, such as images and text data, and (2) for graph-structured data, the underlying connectivity patterns are often complex and diverse. On the other hand, the representation learning has achieved great successes in many areas. Thereby, a potential solution is to learn the representation of graphs in a low-dimensional Euclidean space, such that the graph properties can be preserved. Although tremendous efforts have been made to address the graph representation learning problem, many of them still suffer from their shallow learning mechanisms. Deep learning models on graphs (e.g., graph neural networks) have recently emerged in machine learning and other related areas, and demonstrated the superior performance in various problems. In this survey, despite numerous types of graph neural networks, 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. First, we group the existing graph convolutional network models into two categories based on the types of convolutions and highlight some graph convolutional network models in details. Then, we categorize different graph convolutional networks according to the areas of their applications. Finally, we present several open challenges in this area and discuss potential directions for future research.

Keywords: Aggregation mechanism; Deep learning; Graph convolutional networks; Graph representation learning; Spatial methods; Spectral methods.

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Conflict of interest statement

Competing interestsThe authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
An overview of graph convolutional networks
See this image and copyright information in PMC

References

    1. Backstrom L, Leskovec J. Supervised random walks: predicting and recommending links in social networks. In: WSDM. New York: ACM; 2011. p. 635–44.
    1. Akoglu L, Tong H, Koutra D. Graph based anomaly detection and description: a survey. Data Min Knowl Discov. 2015;29(3):626–88. doi: 10.1007/s10618-014-0365-y. - DOI
    1. Zhang Si, Zhou Dawei, Yildirim Mehmet Yigit, Alcorn Scott, He Jingrui, Davulcu Hasan, Tong Hanghang. Proceedings of the 2017 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics; 2017. HiDDen: Hierarchical Dense Subgraph Detection with Application to Financial Fraud Detection; pp. 570–578.
    1. Li Y, Yu R, Shahabi C, Liu Y. Diffusion convolutional recurrent neural network: data-driven traffic forecasting. 2018.
    1. Monti F, Boscaini D, Masci J, Rodola E, Svoboda J, Bronstein MM. Geometric deep learning on graphs and manifolds using mixture model cnns. In: CVPR, vol. 1. 2017. p. 3.

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