Cited by 1,888Advanced Graph Neural NetworksGraph Theory and AlgorithmsComplex Network Analysis Techniques

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Graph convolutional networks: a comprehensive review

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Zhang, Si, Tong, Hanghang, Xu, Jiejun, Maciejewski, Ross·2019·Computational Social Networks

DOI 10.1186/s40649-019-0069-yPMC PMC10615927

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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.

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Fig. 1 An overview of graph convolutional networksCC BY · PubMed Central

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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Fast R-CNN
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,093

Show all 123 references

Research works printed but not linked: 17

You J, Ying R, Ren X, Hamilton WL, Leskovec J. Graphrnn: a deep generative model for graphs. 2018. arXiv preprint arXiv:1802.08773 · arXiv
Lee JB, Rossi RA, Kim S, Ahmed NK, Koh E. Attention models in graphs: a survey. 2018. arXiv preprint arXiv:1807.07984 · arXiv
Bruna J, Zaremba W, Szlam A, LeCun Y. Spectral networks and locally connected networks on graphs. 2013. arXiv preprint arXiv:1312.6203 · arXiv
Defferrard M, Bresson X, Vandergheynst P. Convolutional neural networks on graphs with fast localized spectral filtering. In: NIPS. 2016. p. 3844–52.
Huang W, Zhang T, Rong Y, Huang J. Adaptive sampling towards fast graph representation learning. In: Advances in neural information processing systems. 2018. p. 4563–72.
Xu B, Shen H, Cao Q, Qiu Y, Cheng X. Graph wavelet neural network. In: International conference on learning representations. 2019. https://openreview.net/forum?id=H1ewdiR5tQ .
Chang J, Gu J, Wang L, Meng G, Xiang S, Pan C. Structure-aware convolutional neural networks. In: Advances in neural information processing systems. 2018. p. 11–20.
Xu K, Li C, Tian, Y, Sonobe, T, Kawarabayashi K.-i, Jegelka S. Representation learning on graphs with jumping knowledge networks. 2018. arXiv preprint arXiv:1806.03536 · arXiv
Dai H, Kozareva Z, Dai B, Smola A, Song L. Learning steady-states of iterative algorithms over graphs. In: International conference on machine learning. 2018. p. 1114–22.
Xu K, Hu W, Leskovec J, Jegelka S. How powerful are graph neural networks? 2018. arXiv preprint arXiv:1810.00826 · arXiv
Gao X, Hu W, Tang J, Pan P, Liu J, Guo Z. Generalized graph convolutional networks for skeleton-based action recognition. 2018. arXiv preprint arXiv:1811.12013 . · arXiv
Zhang T, Zheng W, Cui Z, Li Y. Tensor graph convolutional neural network. 2018; arXiv preprint arXiv:1803.10071 · arXiv
Wang P-S, Liu Y, Guo Y-X, Sun C-Y, Tong X. O-cnn: octree-based convolutional neural networks for 3d shape analysis. ACM Trans Graph (TOG). 2017;36(4):72.
Bastings J, Titov I, Aziz W, Marcheggiani D, Sima’an K. Graph convolutional encoders for syntax-aware neural machine translation. 2017. arXiv preprint arXiv:1704.04675 · DOI · Paper page
Fout A, Byrd J, Shariat B, Ben-Hur A. Protein interface prediction using graph convolutional networks. In: Advances in neural information processing systems. 2017. p. 6530–9.
Harada S, Akita H, Tsubaki M, Baba Y, Takigawa I, Yamanishi Y, Kashima H. Dual convolutional neural network for graph of graphs link prediction. 2018. arXiv preprint arXiv:1810.02080 · arXiv
Chen J, Xu X, Wu Y, Zheng H. Gc-lstm: graph convolution embedded lstm for dynamic link prediction. 2018. arXiv preprint arXiv:1812.04206 · arXiv

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Authors: Zhang, S., Tong, H., Xu, J., & Maciejewski, R.
Affiliations: University of Illinois Urbana-Champaign (US)
HRL Laboratories (United States) (US)
Arizona State University (US)
Corresponding author: Zhang, Si
ORCID: Zhang, Si
Tong, Hanghang
Xu, Jiejun
Maciejewski, Ross
Venue: Computational Social Networks
Publisher: Springer Science and Business Media LLC
Published: 2019-11-10
Volume: 6
Issue: 1
Pages: 11-11
ISSN: 2197-4314 (ISSN-L)
DOI: 10.1186/s40649-019-0069-y
PMC: PMC10615927
OpenAlex: W2985331920
Cited by: 1,888
References: 123
Citations by year: 2026: 271 · 2025: 544 · 2024: 434 · 2023: 308 · 2022: 182 · 2021: 96 · 2020: 52
FWCI: 37.64
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Computational Social Networks (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders: National Science Foundation (IIS-1651203, IIS-1715385, IIS-1743040, CNS-1629888)
Defense Threat Reduction Agency (HDTRA1-16-0017)
United States Air Force and DARPA (FA8750-17-C-0153)
Army Research Office (W911NF-16-1-0168)
U.S. Department of Homeland Security (2017-ST-061-QA0001)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
Physical Sciences › Computer Science › Computational Theory and Mathematics › Graph Theory and Algorithms
Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Keywords: graph convolutional networks, graph neural networks, graph-based deep learning, graph representation learning, graph convolution, graph-structured data, open problems

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