Results 21 to 30 of about 149,463 (272)
Graph Convolutional Networks with EigenPooling [PDF]
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019 Graph neural networks, which generalize deep neural network models to graph structured data, have attracted increasing attention in recent years. They usually learn node representations by transforming, propagating and aggregating node features and have been proven to improve the performance of many graph related tasks such as node classification and ...
Yao Ma 0001 +3 more
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Tangent Graph Convolutional Network
ESANN 2021 proceedings, 2021 Most Graph Convolutions (GCs) proposed in the Graph Neural Networks (GNNs) literature share the principle of computing topologically enriched node representations based on the ones of their neighbors. In this paper, we propose a novel GNN named Tangent Graph Convolutional Network (TGCN) that, in addition to the traditional GC approach, exploits a novel
luca pasa +2 more
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Adaptive Propagation Graph Convolutional Network [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2021 Graph convolutional networks (GCNs) are a family of neural network models that perform inference on graph data by interleaving vertex-wise operations and message-passing exchanges across nodes. Concerning the latter, two key questions arise: (i) how to design a differentiable exchange protocol (e.g., a 1-hop Laplacian smoothing in the original GCN ...
Spinelli I, Scardapane S, Uncini A
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Spiking Graph Convolutional Networks
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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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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Signed Graph Convolutional Networks
2018 IEEE International Conference on Data Mining (ICDM), 2018 Due to the fact much of today's data can be represented as graphs, there has been a demand for generalizing neural network models for graph data. One recent direction that has shown fruitful results, and therefore growing interest, is the usage of graph convolutional neural networks (GCNs). They have been shown to provide a significant improvement on a
Tyler Derr, Yao Ma 0001, Jiliang Tang
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Image Denoising with Graph-Convolutional Neural Networks [PDF]
, 2019 Recovering an image from a noisy observation is a key problem in signal processing. Recently, it has been shown that data-driven approaches employing convolutional neural networks can outperform classical model-based techniques, because they can capture ...
Fracastoro, Giulia +2 more
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Two-way Feature Augmentation Graph Convolution Networks Algorithm [PDF]
Jisuanji kexueGraph convolutional neural network algorithms play a crucial role in the processing of graph structured data.The mainstream mode of existing graph convolutional networks is based on weighted summation of node features using Laplacian matrices,with a ...
LI Mengxi, GAO Xindan, LI Xue
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Factorizable Graph Convolutional Networks
CoRR, 2020 Graphs have been widely adopted to denote structural connections between entities. The relations are in many cases heterogeneous, but entangled together and denoted merely as a single edge between a pair of nodes. For example, in a social network graph, users in different latent relationships like friends and colleagues, are usually connected via a ...
Yiding Yang +3 more
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Dual graph convolutional neural network for predicting chemical networks
BMC Bioinformatics, 2020 Background Predicting of chemical compounds is one of the fundamental tasks in bioinformatics and chemoinformatics, because it contributes to various applications in metabolic engineering and drug discovery.
Shonosuke Harada +6 more
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