The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Computational Capabilities of Graph Neural Networks
Abstract
In this paper, we will consider the approximation properties of a recently introduced neural network model called graph neural network (GNN), which can be used to process-structured data inputs, e.g., acyclic graphs, cyclic graphs, and directed or undirected graphs. This class of neural networks implements a function tau(G,n) is an element of IR(m) that maps a graph G and one of its nodes n onto an m-dimensional Euclidean space. We characterize the functions that can be approximated by GNNs, in probability, up to any prescribed degree of precision. This set contains the maps that satisfy a property called preservation of the unfolding equivalence, and includes most of the practically useful functions on graphs; the only known exception is when the input graph contains particular patterns of symmetries when unfolding equivalence may not be preserved. The result can be considered an extension of the universal approximation property established for the classic feedforward neural networks (FNNs). Some experimental examples are used to show the computational capabilities of the proposed model.
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Cites
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Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582
Self-citationFormal Semantics for Kolmogorov-Arnold Network Representations of Operational GamesPreprint
Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.00826 · Cited by 5,932
A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. (2020) · UTS ePRESS (University of Technology Sydney) · Cited by 3,174
Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification
Wan, S., Gong, C., Zhong, P., Du, B., Zhang, L., & Yang, J. J. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2949180 · Cited by 575
Metadata
- Authors
- Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G.
- Affiliations
- University of Siena (IT)
- Hong Kong Baptist University (HK)
- University of Wollongong (AU)
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2009-01
- Volume
- 20
- Issue
- 1
- Pages
- 81-102
- ISSN
- 1045-9227 (ISSN-L)
- 1941-0093
- OpenAlex
- W2150120952
- Cited by
- 225
- References
- 42
- Citations by year
- 2026: 8 · 2025: 16 · 2024: 25 · 2023: 20 · 2022: 32 · 2021: 34 · 2020: 25 · 2019: 17 · 2018: 8 · 2017: 2 · 2016: 1 · 2015: 3 · 2014: 7 · 2013: 8 · 2012: 4
- FWCI
- 7.93
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Neural Networks (publishedVersion)
- PubMed (publishedVersion)
- HKBU Institutional Repository (Hong Kong Baptist University) (submittedVersion)
- Research Online (University of Wollongong) (submittedVersion)
- Figshare (submittedVersion)
- Use Siena air (University of Siena) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Graph Theory and Algorithms
- Keywords
- graph neural networks, universal approximation, approximation properties, graph symmetry, structured data, acyclic graphs, cyclic graphs, directed and undirected graphs
- MeSH terms
- Algorithms, Pattern Recognition, Automated, Neural Networks, Computer
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