Results 231 to 240 of about 39,156 (266)

Correction to: Coding genomes with gapped pattern graph convolutional network. [PDF]

Bioinformatics
europepmc   +1 more source

Improved graph convolutional network for emotion analysis in social media text. [PDF]

MethodsX
Khemani B, Patil S, Malave S, Gupta J.
europepmc   +1 more source

Identifying T cell antigen at the atomic level with graph convolutional network. [PDF]

Nat Commun
Que J, Xue G, Wang T, Jin X, Wang Z, Cai Y, Yang W, Luo M, Ding Q, Zhang J, Wang Y, Yang Y, Pang F, Hui Y, Wei Z, Xiong J, Xu S, Lin Y, Sun H, Wang P, Xu Z, Jiang Q.   +21 more
europepmc   +1 more source

Multi dynamic temporal representation graph convolutional network for traffic flow prediction. [PDF]

Sci Rep
Wu Z, Liu X, Zhang X.
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.
Fumin Shen, Heng Tao Shen, Wei Liu
exaly   +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
openaire   +2 more sources

Fuzzy Graph Subspace Convolutional Network

IEEE Transactions on Neural Networks and Learning Systems
Graph convolutional networks (GCNs) are a popular approach to learn the feature embedding of graph-structured data, which has shown to be highly effective as well as efficient in performing node classification in an inductive way. However, with massive nongraph-organized data existing in application scenarios nowadays, it is critical to exploit the ...
Jianhang Zhou, Qi Zhang 0059, Shaoning Zeng, Bob Zhang 0001   +3 more
openaire   +2 more sources

Learnable graph convolutional network and feature fusion for multi-view learning

Information Fusion, 2023
Shiping Wang, Zhaoliang Chen, Lele Fu
exaly