Results 241 to 250 of about 145,055 (276)

A bike-sharing demand prediction model based on Spatio-Temporal Graph Convolutional Networks. [PDF]

PeerJ Comput Sci
Zhou C, Hu J, Zhang X, Li Z, Yang K.
europepmc   +1 more source

Development of message passing-based graph convolutional networks for classifying cancer pathology reports. [PDF]

BMC Med Inform Decis Mak
Yoon HJ, Klasky HB, Blanchard AE, Christian JB, Durbin EB, Wu XC, Stroup A, Doherty J, Coyle L, Penberthy L, Tourassi GD.   +10 more
europepmc   +1 more source

DeepMoIC: multi-omics data integration via deep graph convolutional networks for cancer subtype classification. [PDF]

BMC Genomics
Wu J, Chen Z, Xiao S, Liu G, Wu W, Wang S.   +5 more
europepmc   +1 more source

Predicting cortical-thalamic functional connectivity using functional near-infrared spectroscopy and graph convolutional networks. [PDF]

Sci Rep
Tang L, Kebaya LMN, Vahidi H, Meyerink P, de Ribaupierre S, Bhattacharya S, St Lawrence K, Duerden EG.   +7 more
europepmc   +1 more source

Classification of psychosis spectrum disorders using graph convolutional networks with structurally constrained functional connectomes. [PDF]

Neural Netw
Lewis M, Jiang W, Theis ND, Cape J, Prasad KM.   +4 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
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Robust graph learning with graph convolutional network

Information Processing & Management, 2022
Yingying Wan, Changan Yuan 0001, Mengmeng Zhan, Long Chen   +3 more
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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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