Cited by 225Advanced Graph Neural NetworksNeural Networks and ApplicationsGraph Theory and Algorithms

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Computational Capabilities of Graph Neural Networks

Scarselli, F., Gori, M., Ah Chung Tsoi, Hagenbuchner, M., Monfardini, G.·2009·IEEE Transactions on Neural Networks

DOI 10.1109/tnn.2008.2005141

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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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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)
ORCID: Scarselli, F.
Gori, M.
Ah Chung Tsoi
Hagenbuchner, M.
Venue: IEEE Transactions on Neural Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2009-01
Volume: 20
Issue: 1
Pages: 81-102
ISSN: 1045-9227 (ISSN-L)
1941-0093
DOI: 10.1109/tnn.2008.2005141
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
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
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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Computational Capabilities of Graph Neural Networks

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