Graph Neural Networks: Architectures, Stability, and Transferability
Graph neural networks (GNNs) are information processing architectures for signals supported on graphs. They are presented here as generalizations of convolutional neural networks (CNNs) in which individual layers contain banks of graph convolutional filters instead of banks of classical convolutiona...
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| Vydané v: | Proceedings of the IEEE Ročník 109; číslo 5; s. 660 - 682 |
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| Médium: | Journal Article |
| Jazyk: | English |
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New York
IEEE
01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0018-9219, 1558-2256 |
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| Abstract | Graph neural networks (GNNs) are information processing architectures for signals supported on graphs. They are presented here as generalizations of convolutional neural networks (CNNs) in which individual layers contain banks of graph convolutional filters instead of banks of classical convolutional filters. Otherwise, GNNs operate as CNNs. Filters are composed of pointwise nonlinearities and stacked in layers. It is shown that GNN architectures exhibit equivariance to permutation and stability to graph deformations. These properties help explain the good performance of GNNs that can be observed empirically. It is also shown that if graphs converge to a limit object, a graphon, GNNs converge to a corresponding limit object, a graphon neural network. This convergence justifies the transferability of GNNs across networks with different numbers of nodes. Concepts are illustrated by the application of GNNs to recommendation systems, decentralized collaborative control, and wireless communication networks. |
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| AbstractList | Graph neural networks (GNNs) are information processing architectures for signals supported on graphs. They are presented here as generalizations of convolutional neural networks (CNNs) in which individual layers contain banks of graph convolutional filters instead of banks of classical convolutional filters. Otherwise, GNNs operate as CNNs. Filters are composed of pointwise nonlinearities and stacked in layers. It is shown that GNN architectures exhibit equivariance to permutation and stability to graph deformations. These properties help explain the good performance of GNNs that can be observed empirically. It is also shown that if graphs converge to a limit object, a graphon, GNNs converge to a corresponding limit object, a graphon neural network. This convergence justifies the transferability of GNNs across networks with different numbers of nodes. Concepts are illustrated by the application of GNNs to recommendation systems, decentralized collaborative control, and wireless communication networks. |
| Author | Gama, Fernando Ruiz, Luana Ribeiro, Alejandro |
| Author_xml | – sequence: 1 givenname: Luana orcidid: 0000-0002-9666-1211 surname: Ruiz fullname: Ruiz, Luana email: rubruiz@seas.upenn.edu organization: Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA – sequence: 2 givenname: Fernando orcidid: 0000-0001-6117-8193 surname: Gama fullname: Gama, Fernando email: fgama@berkeley.edu organization: Electrical Engineering and Computer Sciences Department, University of California at Berkeley, Berkeley, CA, USA – sequence: 3 givenname: Alejandro orcidid: 0000-0003-4230-9906 surname: Ribeiro fullname: Ribeiro, Alejandro email: aribeiro@seas.upenn.edu organization: Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA |
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| SubjectTerms | Artificial neural networks Communication networks Convergence Convolutional neural networks Data processing Deep learning Equivariance Filter banks graph filters Graph neural networks graph neural networks (GNNs) graph signal processing (GSP) graphon neural networks graphons Graphs Information filters Information processing Neural networks Permutations Probability distribution Recommender systems Signal processing Stability Stability analysis Training data transferability Wireless communications Wireless networks |
| Title | Graph Neural Networks: Architectures, Stability, and Transferability |
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