Results 11 to 20 of about 3,278,052 (298)
Distributed Training of Graph Convolutional Networks [PDF]
IEEE Transactions on Signal and Information Processing over Networks, 2021 Published on IEEE Transactions on Signal and Information Processing over ...
Scardapane S, Spinelli I, Di Lorenzo P
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Deformable Graph Convolutional Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 Graph neural networks (GNNs) have significantly improved the representation power for graph-structured data. Despite of the recent success of GNNs, the graph convolution in most GNNs have two limitations. Since the graph convolution is performed in a small local neighborhood on the input graph, it is inherently incapable to capture long-range ...
Jinyoung Park 0005 +3 more
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Convolutional Graph Neural Networks
2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019 Convolutional neural networks (CNNs) restrict the, otherwise arbitrary, linear operation of neural networks to be a convolution with a bank of learned filters. This makes them suitable for learning tasks based on data that exhibit the regular structure of time signals and images.
Fernando Gama +3 more
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Spiking Graph Convolutional Networks [PDF]
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022 Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable representation ability in learning the graph information. However, GCNs, when implemented on a deep network, require expensive computation power, making them difficult to be deployed on battery-powered devices.
Zulun Zhu +5 more
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Graph Convolutional Networks for Road Networks [PDF]
Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2019 Machine learning techniques for road networks hold the potential to facilitate many important transportation applications. Graph Convolutional Networks (GCNs) are neural networks that are capable of leveraging the structure of a road network by utilizing information of, e.g., adjacent road segments.
Tobias Skovgaard Jepsen +2 more
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Limitless stability for Graph Convolutional Networks
CoRR, 2023 This work establishes rigorous, novel and widely applicable stability guarantees and transferability bounds for graph convolutional networks -- without reference to any underlying limit object or statistical distribution. Crucially, utilized graph-shift operators (GSOs) are not necessarily assumed to be normal, allowing for the treatment of networks on
Koke, Christian
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Adaptive Graph Convolutional Neural Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2018 Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and connectivity.
Ruoyu Li 0002 +3 more
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Linear Graph Convolutional Networks. [PDF]
, 2020 Many neural networks for graphs are based on the graph convolution operator, proposed more than a decade ago. Since then, many alternative definitions have been proposed, that tend to add complexity (and non-linearity) to the model. In this paper, we follow the opposite direction by proposing a linear graph convolution operator. Despite its simplicity,
Navarin N., Erb W., Pasa L., Sperduti A.
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Review of Blockchain Application With Graph Neural Networks, Graph Convolutional Networks and Convolutional Neural Networks [PDF]
This paper reviews the applications of Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs), and Convolutional Neural Networks (CNNs) in blockchain technology. As the complexity and adoption of blockchain networks continue to grow, traditional analytical methods are proving inadequate in capturing the intricate relationships and dynamic ...
Amy Ancelotti, Claudia Liason
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Convolutional Kernel Networks for Graph-Structured Data [PDF]
, 2020 International audienceWe introduce a family of multilayer graph kernels and establish new links between graph convolutional neural networks and kernel methods.
Chen, Dexiong +2 more
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