Results 261 to 270 of about 3,278,052 (298)

Few-shot traffic classification based on autoencoder and deep graph convolutional networks. [PDF]

Sci Rep
Xu S, Han J, Liu Y, Liu H, Bai Y.
europepmc   +1 more source

Traffic flow prediction based on spatial-temporal multi factor fusion graph convolutional networks. [PDF]

Sci Rep
Chen YT, Liu A, Li C, Li S, Yang X.
europepmc   +1 more source

A knowledge tracing approach with dual graph convolutional networks and positive/negative feature enhancement network. [PDF]

PLoS One
Wang J, Tang Q, Zheng Z.
europepmc   +1 more source

Secondary Operation Risk Assessment Method Integrating Graph Convolutional Networks and Semantic Embeddings. [PDF]

Sensors (Basel)
Zhu P, Li Y, Xu P, Li P, Zhao Z, Li G.
europepmc   +1 more source

An interpretable adaptive edge-weighted graph convolutional networks model for physical fitness assessment of primary school student in high-altitude regions. [PDF]

Sci Rep
Wang B, Xiao Y, Yang X, Zhang X.
europepmc   +1 more source

Predicting Organ-Specific Toxicity of Selective Androgen Receptor Modulators, using Transfer Learning on Graph Convolutional Networks

Kalian AD, Silva AC, Lee J, Dorne JC, Potter C, Benfenati E, Osborne OJ, Guo M, Hogstrand C.   +8 more
europepmc   +1 more source

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Graph sparsification with graph convolutional networks

International Journal of Data Science and Analytics, 2021
Graphs are ubiquitous across the globe and within science and engineering. Some powerful classifiers are proposed to classify nodes in graphs, such as Graph Convolutional Networks (GCNs). However, as graphs are growing in size, node classification on large graphs can be space and time consuming due to using whole graphs.
Jiayu Li 0002, Tianyun Zhang, Hao Tian, Shengmin Jin, Makan Fardad, Reza Zafarani   +5 more
openaire   +1 more source

Graph Convolutional Network Hashing

IEEE Transactions on Cybernetics, 2020
Recently, graph-based hashing that learns similarity-preserving binary codes via an affinity graph has been extensively studied for large-scale image retrieval. However, most graph-based hashing methods resort to intractable binary quadratic programs, making them unscalable to massive data.
Xiang Zhou 0008, Fumin Shen, Li Liu 0004, Wei Liu 0005, Liqiang Nie, Yang Yang 0002, Heng Tao Shen   +6 more
openaire   +3 more sources

Graph Convolutional Kernel Machine versus Graph Convolutional Networks

Advances in Neural Information Processing Systems 36, 2023
Zhihao Wu 0002, Zhao Zhang 0001, Jicong Fan 0001   +2 more
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GCN-Denoiser: Mesh Denoising with Graph Convolutional Networks

ACM Transactions on Graphics, 2022
Denis Zorin, Evgeny Burnaev, Hongbo Fu   +2 more
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