14.5.8.6.1 Graph Convolutional Neural Networks

Fu, S.[Sichao], Liu, W.F.[Wei-Feng], Li, S.Y.[Shu-Ying], Zhou, Y.C.[Yi-Cong],
Two-order graph convolutional networks for semi-supervised classification,
IET-IPR(13), No. 14, 12 December 2019, pp. 2763-2771.
DOI Link 1912
BibRef

Zhang, Z.H.[Zhi-Hong], Chen, D.D.[Dong-Dong], Wang, J.J.[Jian-Jia], Bai, L.[Lu], Hancock, E.R.[Edwin R.],
Quantum-based subgraph convolutional neural networks,
PR(88), 2019, pp. 38-49.
Elsevier DOI 1901
Graph convolutional neural networks, Spatial construction, Quantum walks, Subgraph BibRef

Xu, C.Y.[Chuan-Yu], Wang, D.[Dong], Zhang, Z.H.[Zhi-Hong], Wang, B.[Beizhan], Zhou, D.[Da], Ren, G.J.[Gui-Jun], Bai, L.[Lu], Cui, L.X.[Li-Xin], Hancock, E.R.[Edwin R.],
Depth-based Subgraph Convolutional Neural Networks,
ICPR18(1024-1029)
IEEE DOI 1812
Convolution, Feature extraction, Convolutional neural networks, Standards, Task analysis, Data mining, Laplace equations BibRef

Zhang, Z.H.[Zhi-Hong], Chen, D.D.[Dong-Dong], Wang, Z.[Zeli], Li, H.[Heng], Bai, L.[Lu], Hancock, E.R.[Edwin R.],
Depth-based subgraph convolutional auto-encoder for network representation learning,
PR(90), 2019, pp. 363-376.
Elsevier DOI 1903
Graph based CNN style learning. Network representation learning, Graph convolutional neural network, Node classification BibRef

Chen, Y.X.[Yu-Xin], Ma, G.[Gaoqun], Yuan, C.F.[Chun-Feng], Li, B.[Bing], Zhang, H.[Hui], Wang, F.[Fangshi], Hu, W.M.[Wei-Ming],
Graph convolutional network with structure pooling and joint-wise channel attention for action recognition,
PR(103), 2020, pp. 107321.
Elsevier DOI 2005
Graph convolutional network, Structure graph pooling, Joint-wise channel attention BibRef

Luo, Y.[Yawei], Ji, R.R.[Rong-Rong], Guan, T.[Tao], Yu, J.Q.[Jun-Qing], Liu, P.[Ping], Yang, Y.[Yi],
Every node counts: Self-ensembling graph convolutional networks for semi-supervised learning,
PR(106), 2020, pp. 107451.
Elsevier DOI 2006
Teacher-student models, Self-ensemble learning, Graph convolutional networks, Semi-supervised learning BibRef

Wu, J.X.[Jia-Xin], Zhong, S.H.[Sheng-Hua], Liu, Y.[Yan],
Dynamic graph convolutional network for multi-video summarization,
PR(107), 2020, pp. 107382.
Elsevier DOI 2008
Multi-video summarization, Graph convolutional network, Class imbalance problem BibRef

Yu, B.[Bin], Hu, J.Z.[Jin-Zhi], Xie, Y.[Yu], Zhang, C.[Chen], Tang, Z.H.[Zhou-Hua],
Rich heterogeneous information preserving network representation learning,
PR(108), 2020, pp. 107564.
Elsevier DOI 2008
Network representation learning, Heterogeneous information, Autoencoder BibRef

Liu, Y.S.[Yong-Sheng], Chen, W.Y.[Wen-Yu], Qu, H.[Hong], Mahmud, S.M.H.[S.M. Hasan], Miao, K.[Kebin],
Weakly supervised image classification and pointwise localization with graph convolutional networks,
PR(109), 2021, pp. 107596.
Elsevier DOI 2009
Deep learning, Learning systems, Convolutional neural networks, Predictive models, Image classification, Graph theory BibRef

Wang, H., Zou, Y., Chong, D., Wang, W.,
Modeling Label Dependencies for Audio Tagging With Graph Convolutional Network,
SPLetters(27), 2020, pp. 1560-1564.
IEEE DOI 2009
Tagging, Acoustics, Spectrogram, Training, Convolution, Symmetric matrices, Probability, Audio tagging, label dependencies, representation learning BibRef

Chang, J.L.[Jian-Long], Wang, L.F.[Ling-Feng], Meng, G.F.[Gao-Feng], Zhang, Q.[Qi], Xiang, S.M.[Shi-Ming], Pan, C.H.[Chun-Hong],
Local-Aggregation Graph Networks,
PAMI(42), No. 11, November 2020, pp. 2874-2886.
IEEE DOI 2010
BibRef
Earlier: A1, A2, A3, A5, A6, Only:
Deep Adaptive Image Clustering,
ICCV17(5880-5888)
IEEE DOI 1802
Convolution, Neural networks, Message passing, Laplace equations, Aggregates, Pattern recognition, Function approximation, non-Euclidean structured signal. feature extraction, image classification, iterative methods, Training BibRef

Liu, Y.B.[Yan-Bei], Wang, Q.[Qi], Wang, X.[Xiao], Zhang, F.[Fang], Geng, L.[Lei], Wu, J.[Jun], Xiao, Z.T.[Zhi-Tao],
Community enhanced graph convolutional networks,
PRL(138), 2020, pp. 462-468.
Elsevier DOI 1806
Graph representation learning, Community structure, Graph convolutional networks BibRef

Li, Q.[Qing], Peng, X.J.[Xiao-Jiang], Qiao, Y.[Yu], Peng, Q.A.[Qi-Ang],
Learning label correlations for multi-label image recognition with graph networks,
PRL(138), 2020, pp. 378-384.
Elsevier DOI 1806
Multi-label image recognition, Graph convolutional networks, Label correlation graph, Sparse correlation constraint BibRef

Ye, J.[Jin], He, J.J.[Jun-Jun], Peng, X.J.[Xiao-Jiang], Wu, W.H.[Wen-Hao], Qiao, Y.[Yu],
Attention-driven Dynamic Graph Convolutional Network for Multi-label Image Recognition,
ECCV20(XXI:649-665).
Springer DOI 2011
BibRef

Bai, S.[Song], Zhang, F.H.[Fei-Hu], Torr, P.H.S.[Philip H.S.],
Hypergraph convolution and hypergraph attention,
PR(110), 2021, pp. 107637.
Elsevier DOI 2011
Graph learning, Hypergraph learning, Graph neural networks, Semi-supervised learning BibRef

Gama, F., Isufi, E., Leus, G., Ribeiro, A.,
Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks,
SPMag(37), No. 6, November 2020, pp. 128-138.
IEEE DOI 2011
Convolution, Finite impulse response filters, Autoregressive processes, Network topology, Information filters, Graphical models BibRef

Iddianozie, C.[Chidubem], McArdle, G.[Gavin],
Improved Graph Neural Networks for Spatial Networks Using Structure-Aware Sampling,
IJGI(9), No. 11, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Li, Y.S.[Yan-Sheng], Chen, R.X.[Rui-Xian], Zhang, Y.J.[Yong-Jun], Zhang, M.[Mi], Chen, L.[Ling],
Multi-Label Remote Sensing Image Scene Classification by Combining a Convolutional Neural Network and a Graph Neural Network,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Dong, W.[Wei], Wu, J.S.[Jun-Sheng], Bai, Z.W.[Zong-Wen], Hu, Y.[Yaoqi], Li, W.G.[Wei-Gang], Qiao, W.[Wei], Wozniak, M.[Marcin],
MobileGCN applied to low-dimensional node feature learning,
PR(112), 2021, pp. 107788.
Elsevier DOI 2102
Graph convolutional networks, Affinity-aware encoding, Updater, Depth-wise separable graph convolution, Low-Dimensional node features BibRef

Sun, B., Zhang, H., Wu, Z., Zhang, Y., Li, T.,
Adaptive Spatiotemporal Graph Convolutional Networks for Motor Imagery Classification,
SPLetters(28), 2021, pp. 219-223.
IEEE DOI 2102
Convolution, Electroencephalography, Spatiotemporal phenomena, Feature extraction, Electrodes, Task analysis, Adaptive systems, spatiotemporal structure BibRef

Pu, S.L.[Sheng-Liang], Wu, Y.F.[Yuan-Feng], Sun, X.[Xu], Sun, X.T.[Xiao-Tong],
Hyperspectral Image Classification with Localized Graph Convolutional Filtering,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Jiang, J.J.[Jun-Jie], He, Z.X.[Zai-Xing], Zhang, S.Y.[Shu-You], Zhao, X.Y.[Xin-Yue], Tan, J.R.[Jian-Rong],
Learning to transfer focus of graph neural network for scene graph parsing,
PR(112), 2021, pp. 107707.
Elsevier DOI 2102
Semantic relationship, Graphical focus, Scene graph, Class imbalance, Image understanding BibRef

Ruiz, L.[Luana], Gama, F.[Fernando], Ribeiro, A.[Alejandro],
Graph Neural Networks: Architectures, Stability, and Transferability,
PIEEE(109), No. 5, May 2021, pp. 660-682.
IEEE DOI 2105
Training, Stability analysis, Convolution, Neural networks, Transforms, Strain, Probability distribution, Equivariance, transferability BibRef

Nie, W.Z.[Wei-Zhi], Ren, M.J.[Min-Jie], Liu, A.A.[An-An], Mao, Z.D.[Zhen-Dong], Nie, J.[Jie],
M-GCN: Multi-Branch Graph Convolution Network for 2D Image-based on 3D Model Retrieval,
MultMed(23), 2021, pp. 1962-1976.
IEEE DOI 2107
Solid modeling, Computational modeling, Visualization, multiple graphs BibRef

Manessi, F.[Franco], Rozza, A.[Alessandro],
Graph-based neural network models with multiple self-supervised auxiliary tasks,
PRL(148), 2021, pp. 15-21.
Elsevier DOI 2107
Graph neural networks, Self-supervised learning, Multi-task learning, Graph convolutional networks, Semi-supervised learning BibRef

Martineau, M.[Maxime], Raveaux, R.[Romain], Conte, D.[Donatello], Venturini, G.[Gilles],
Graph matching as a graph convolution operator for graph neural networks,
PRL(149), 2021, pp. 59-66.
Elsevier DOI 2108
BibRef

Wang, W.[Wei], Gao, J.Y.[Jun-Yu], Yang, X.[Xiaoshan], Xu, C.S.[Chang-Sheng],
Learning Coarse-to-Fine Graph Neural Networks for Video-Text Retrieval,
MultMed(23), 2021, pp. 2386-2397.
IEEE DOI 2108
Feature extraction, Encoding, Task analysis, Semantics, Data models, Cognition, Focusing, Video-text retrieval, graph neural network, coarse-to-fine strategy BibRef

Cao, P.P.[Ping-Ping], Chen, P.[Pengpeng], Niu, Q.[Qiang],
Multi-label image recognition with two-stream dynamic graph convolution networks,
IVC(113), 2021, pp. 104238.
Elsevier DOI 2108
Multi-label image recognition, Two streams, Reconstructing graph feature nodes, Dynamic graph convolution networks BibRef

Zhang, Z.[Zhong], Zhang, H.J.[Hai-Jia], Liu, S.[Shuang], Xie, Y.[Yuan], Durrani, T.S.[Tariq S.],
Part-guided graph convolution networks for person re-identification,
PR(120), 2021, pp. 108155.
Elsevier DOI 2109
Person re-identification, Graph convolution network BibRef

Wang, J.[Jie], Liang, J.[Jiye], Yao, K.X.[Kai-Xuan], Liang, J.Q.[Jian-Qing], Wang, D.H.[Dian-Hui],
Graph convolutional autoencoders with co-learning of graph structure and node attributes,
PR(121), 2022, pp. 108215.
Elsevier DOI 2109
Graph representation learning, Graph convolutional autoencoders, Graph filter BibRef

Jiang, B.[Bo], Sun, P.F.[Peng-Fei], Luo, B.[Bin],
GLMNet: Graph learning-matching convolutional networks for feature matching,
PR(121), 2022, pp. 108167.
Elsevier DOI 2109
Graph matching, Graph learning, Graph convolutional network, Laplacian sharpening BibRef

Hoang, N.T., Maehara, T.[Takanori], Murata, T.[Tsuyoshi],
Revisiting Graph Neural Networks: Graph Filtering Perspective,
ICPR21(8376-8383)
IEEE DOI 2105
Convolutional codes, Analytical models, Filtering, Convolution, Graph neural networks BibRef

Lyu, Y.C.[Ye-Cheng], Li, M.[Ming], Huang, X.M.[Xin-Ming], Guler, U.[Ulkuhan], Schaumont, P.[Patrick], Zhang, Z.[Ziming],
TreeRNN: Topology-Preserving Deep Graph Embedding and Learning,
ICPR21(7493-7499)
IEEE DOI 2105
NN learning of grap structues. Image segmentation, Recurrent neural networks, Convolution, Message passing, Image representation BibRef

Dominguez, M.[Miguel], Ptucha, R.[Raymond],
Directional Graph Networks with Hard Weight Assignments,
ICPR21(7439-7446)
IEEE DOI 2105
Convolution, Computational modeling, Neural networks, Robot sensing systems, Computational efficiency, Sensors BibRef

Zhang, Y.H.[Yu-Hang], Ren, H.S.[Hong-Shuai], Ye, J.X.[Jie-Xia], Gao, X.T.[Xi-Tong], Wang, Y.[Yang], Ye, K.J.[Ke-Jiang], Xu, C.Z.[Cheng-Zhong],
AOAM: Automatic Optimization of Adjacency Matrix for Graph Convolutional Network,
ICPR21(5130-5136)
IEEE DOI 2105
Training, Correlation, Convolution, Heuristic algorithms, Focusing, Reinforcement learning, Search problems, Node Information Entropy BibRef

Sahbi, H.[Hichem],
Kernel-based Graph Convolutional Networks,
ICPR21(4887-4894)
IEEE DOI 2105
Training, Convolution, Image color analysis, Training data, Hilbert space, Kernel BibRef

Li, Z.X.[Zhi-Xin], Sun, Y.[Yaru], Tang, S.[Suqin], Zhang, C.L.[Can-Long], Ma, H.F.[Hui-Fang],
Reinforcement Learning with Dual Attention Guided Graph Convolution for Relation Extraction,
ICPR21(946-953)
IEEE DOI 2105
Convolution, Aggregates, Semantics, Reinforcement learning, Information representation, Feature extraction, Cognition BibRef

Carbonell, M.[Manuel], Riba, P.[Pau], Villegas, M.[Mauricio], Fornés, A.[Alicia], Lladós, J.[Josep],
Named Entity Recognition and Relation Extraction with Graph Neural Networks in Semi Structured Documents,
ICPR21(9622-9627)
IEEE DOI 2105
Information retrieval, Graph neural networks, Data mining, Task analysis, Tuning, Testing, Strain, Graph Neural Networks BibRef

Deng, J.[Jiehui], Wan, S.[Sheng], Wang, X.[Xiang], Tu, E.[Enmei], Huang, X.L.[Xiao-Lin], Yang, J.[Jie], Gong, C.[Chen],
Edge-Aware Graph Attention Network for Ratio of Edge-User Estimation in Mobile Networks,
ICPR21(9988-9995)
IEEE DOI 2105
Deep learning, Fuses, Estimation, Switches, Pattern recognition BibRef

Tian, Y.X.[Yu-Xing], Liu, Z.[Zheng], Liu, W.[Weiding], Zhang, Z.[Zeyu], Qu, Y.[Yanwen],
What nodes vote to? Graph classification without readout phase,
ICPR21(8439-8445)
IEEE DOI 2105
Message passing, Logic gates, Benchmark testing, Feature extraction, Graph neural networks, Decoding, graph neural networks BibRef

Wang, C.[Chen], Deng, C.Y.[Cheng-Yuan],
On the Global Self-attention Mechanism for Graph Convolutional Networks,
ICPR21(8531-8538)
IEEE DOI 2105
Benchmark testing, Pattern recognition, Convolutional neural networks, Task analysis BibRef

Yang, L.[Lei], Huang, Q.Q.[Qing-Qiu], Huang, H.Y.[Huai-Yi], Xu, L.N.[Lin-Ning], Lin, D.[Dahua],
Learn to Propagate Reliably on Noisy Affinity Graphs,
ECCV20(XV:447-464).
Springer DOI 2011
exploiting unlabeled data. BibRef

Park, H., Jeong, M., Kim, Y., Kim, C.,
Self-Training Of Graph Neural Networks Using Similarity Reference For Robust Training With Noisy Labels,
ICIP20(1951-1955)
IEEE DOI 2011
Training, Sampling methods, Noise measurement, Feature extraction, Training data, Indexes, Data mining, Noisy label, sampling method, graph-based CNN. BibRef

Adaloglou, N.[Nikolas], Vretos, N.[Nicholas], Daras, P.[Petros],
Multi-view Adaptive Graph Convolutions for Graph Classification,
ECCV20(XXVI:398-414).
Springer DOI 2011
BibRef

Zhang, X.K.[Xi-Kun], Xu, C.[Chang], Tao, D.C.[Da-Cheng],
On Dropping Clusters to Regularize Graph Convolutional Neural Networks,
ECCV20(XXI:245-260).
Springer DOI 2011
BibRef

Korban, M.[Matthew], Li, X.[Xin],
Ddgcn: A Dynamic Directed Graph Convolutional Network for Action Recognition,
ECCV20(XX:761-776).
Springer DOI 2011
BibRef

Yu, C.Q.[Chang-Qian], Liu, Y.F.[Yi-Fan], Gao, C.X.[Chang-Xin], Shen, C.H.[Chun-Hua], Sang, N.[Nong],
Representative Graph Neural Network,
ECCV20(VII:379-396).
Springer DOI 2011
BibRef

Iscen, A.[Ahmet], Tolias, G.[Giorgos], Avrithis, Y.[Yannis], Chum, O.[Ondrej], Schmid, C.[Cordelia],
Graph Convolutional Networks for Learning with Few Clean and Many Noisy Labels,
ECCV20(X:286-302).
Springer DOI 2011
BibRef

Wang, C.[Chu], Samari, B.[Babak], Kim, V.G.[Vladimir G.], Chaudhuri, S.[Siddhartha], Siddiqi, K.[Kaleem],
Affinity Graph Supervision for Visual Recognition,
CVPR20(8244-8252)
IEEE DOI 2008
Visualization, Training, Task analysis, Manganese, Proposals, Computer architecture, Convolutional neural networks BibRef

Xu, Q.G.[Qian-Geng], Sun, X.D.[Xu-Dong], Wu, C.Y.[Cho-Ying], Wang, P.Q.[Pan-Qu], Neumann, U.[Ulrich],
Grid-GCN for Fast and Scalable Point Cloud Learning,
CVPR20(5660-5669)
IEEE DOI 2008
Computational modeling, Data models, Convolution, Aggregates, Task analysis, Feature extraction BibRef

Wei, X., Yu, R., Sun, J.,
View-GCN: View-Based Graph Convolutional Network for 3D Shape Analysis,
CVPR20(1847-1856)
IEEE DOI 2008
Shape, Convolution, Feature extraction, Aggregates, Image recognition BibRef

You, Y., Chen, T., Wang, Z., Shen, Y.,
L2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks,
CVPR20(2124-2132)
IEEE DOI 2008
Training, Time complexity, Convolution, Memory management, Prediction algorithms, Clustering algorithms BibRef

Yang, Q., Li, C., Dai, W., Zou, J., Qi, G., Xiong, H.,
Rotation Equivariant Graph Convolutional Network for Spherical Image Classification,
CVPR20(4302-4311)
IEEE DOI 2008
Convolution, Kernel, Solid modeling, Distortion, Image quality, Convolutional neural networks BibRef

Lin, J., Yuan, Y., Shao, T., Zhou, K.,
Towards High-Fidelity 3D Face Reconstruction From In-the-Wild Images Using Graph Convolutional Networks,
CVPR20(5890-5899)
IEEE DOI 2008
Face, Shape, Image reconstruction, Image color analysis, Rendering (computer graphics), Feature extraction BibRef

Zhang, K.H.[Kai-Hua], Li, T.P.[Teng-Peng], Shen, S.W.[Shi-Wen], Liu, B.[Bo], Chen, J.[Jin], Liu, Q.S.[Qing-Shan],
Adaptive Graph Convolutional Network With Attention Graph Clustering for Co-Saliency Detection,
CVPR20(9047-9056)
IEEE DOI 2008
Feature extraction, Task analysis, Adaptive systems, Decoding, Saliency detection, Visualization, Convolution BibRef

Lin, Z., Huang, S., Wang, Y.F.,
Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis,
CVPR20(1797-1806)
IEEE DOI 2008
Convolution, Kernel, Feature extraction, Shape, Task analysis BibRef

Park, J.[Jiwoong], Lee, M.[Minsik], Chang, H.J.[Hyung Jin], Lee, K.[Kyuewang], Choi, J.Y.[Jin Young],
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning,
ICCV19(6518-6527)
IEEE DOI 2004
data visualisation, decoding, encoding, graph theory, image representation, learning (artificial intelligence), BibRef

Mosella-Montoro, A., Ruiz-Hidalgo, J.,
Residual Attention Graph Convolutional Network for Geometric 3D Scene Classification,
GMDL19(4123-4132)
IEEE DOI 2004
computational geometry, convolutional neural nets, feature extraction, image classification, image colour analysis, agc BibRef

Sun, H.L.[Hao-Liang], Zhen, X.T.[Xian-Tong], Yin, Y.L.[Yi-Long],
Learning the Set Graphs: Image-Set Classification Using Sparse Graph Convolutional Networks,
ICIP19(4554-4558)
IEEE DOI 1910
Set graph learning, Graph convolutional network, l1,2-Norm, Image-set classification BibRef

Chen, Z.M.[Zhao-Min], Wei, X.S.[Xiu-Shen], Wang, P.[Peng], Guo, Y.[Yanwen],
Multi-Label Image Recognition With Graph Convolutional Networks,
CVPR19(5172-5181).
IEEE DOI 2002
BibRef

Zhang, L.[Ling], Zhu, Z.G.[Zhi-Gang],
Unsupervised Feature Learning for Point Cloud Understanding by Contrasting and Clustering Using Graph Convolutional Neural Networks,
3DV19(395-404)
IEEE DOI 1911
Task analysis, Feature extraction, Training, Unsupervised learning, Semantics, Graph convolutional neural network BibRef

Litany, O., Bronstein, A., Bronstein, M., Makadia, A.,
Deformable Shape Completion with Graph Convolutional Autoencoders,
CVPR18(1886-1895)
IEEE DOI 1812
Shape, Task analysis, Training, Strain, Neural networks BibRef

Verma, N.[Nitika], Boyer, E.[Edmond], Verbeek, J.[Jakob],
FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis,
CVPR18(2598-2606)
IEEE DOI 1812
Shape, Convolution, Standards, Visualization, Neural networks BibRef

Edwards, M.[Michael], Xie, X.H.[Xiang-Hua],
Graph Convolutional Neural Network,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Convolutional Neural Networks, Design, Implementation Issues .