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Graph convolutional networks: a comprehensive review
Open accessAbstract
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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“The introduction of graph convolutional network (GCN) has enabled GNNs to learn the latent representations of target datasets with a convolution procedure [65].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [65]
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Metadata
- 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
- Venue
- Computational Social Networks
- Published
- 2019-11-10
- Volume
- 6
- Issue
- 1
- Pages
- 11-11
- ISSN
- 2197-4314 (ISSN-L)
- 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
- 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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