Results 231 to 240 of about 38,896 (265)

EGCN: Entropy-based graph convolutional network for anomalous pattern detection and forecasting in real estate markets. [PDF]

PLoS One
Le D, Rajasegarar S, Luo W, Nguyen TT, Vo N, Nguyen Q, Angelova M.   +6 more
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

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

Bioinformatics
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

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

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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, Fumin Shen, Ｗei Liu
exaly   +3 more sources

Robust graph learning with graph convolutional network

Information Processing and Management, 2022
Mengmeng Zhan
exaly   +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