14.5.10.7.2 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.Z.[Bei-Zhan], 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.B.[Ke-Bin],
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

Dong, W.[Wei], Wu, J.S.[Jun-Sheng], Bai, Z.W.[Zong-Wen], Hu, Y.Q.[Yao-Qi], 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

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

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

Cao, P.P.[Ping-Ping], Chen, P.P.[Peng-Peng], 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

Mesgaran, M.[Mahsa], Hamza, A.B.[A. Ben],
Anisotropic Graph Convolutional Network for Semi-Supervised Learning,
MultMed(23), 2021, pp. 3931-3942.
IEEE DOI 2112
Convolution, Task analysis, Laplace equations, Smoothing methods, Semisupervised learning, Anisotropic magnetoresistance, Geometry, classification BibRef

Bai, L.[Lu], Cui, L.X.[Li-Xin], Jiao, Y.H.[Yu-Hang], Rossi, L.[Luca], Hancock, E.R.[Edwin R.],
Learning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph Classification,
PAMI(44), No. 2, February 2022, pp. 783-798.
IEEE DOI 2201
Convolution, Adaptation models, Transforms, Convolutional neural networks, Standards, Feature extraction, backtrackless walk BibRef

Kim, Y.[Youngeun], Hong, S.[Sungeun],
Adaptive Graph Adversarial Networks for Partial Domain Adaptation,
CirSysVideo(32), No. 1, January 2022, pp. 172-182.
IEEE DOI 2201
Handheld computers, Training, Standards, Task analysis, Deep learning, Convolution, Adaptive systems, graph convolutional networks BibRef

Kenning, M.[Michael], Deng, J.J.[Jing-Jing], Edwards, M.[Michael], Xie, X.H.[Xiang-Hua],
A directed graph convolutional neural network for edge-structured signals in link-fault detection,
PRL(153), 2022, pp. 100-106.
Elsevier DOI 2201
Graph deep learning, Datacenter, Directed graph, Edge signals, Graph edge learning, Linegraphs, Directed linegraphs, Graph convolution BibRef

Yang, Z.Q.[Zi-Qing], Han, S.[Shoudong], Zhao, J.[Jun],
Poisson kernel: Avoiding self-smoothing in graph convolutional networks,
PR(124), 2022, pp. 108443.
Elsevier DOI 2203
Graph convolutional kernel, Graph convolutional network, Graph neural network, Graph structure, Self-smoothing BibRef

Zheng, R.G.[Rui-Gang], Chen, W.F.[Wei-Fu], Feng, G.[Guocan],
Semi-supervised node classification via adaptive graph smoothing networks,
PR(124), 2022, pp. 108492.
Elsevier DOI 2203
Adaptive graph smoothing networks, Graph convolutional networks, Semi-supervised learning, Graph node classification BibRef

Dong, X.F.[Xin-Feng], Liu, L.[Li], Zhu, L.[Lei], Nie, L.Q.[Li-Qiang], Zhang, H.X.[Hua-Xiang],
Adversarial Graph Convolutional Network for Cross-Modal Retrieval,
CirSysVideo(32), No. 3, March 2022, pp. 1634-1645.
IEEE DOI 2203
Semantics, Feature extraction, Task analysis, Generative adversarial networks, Correlation, Generators BibRef

Zhao, Y.[Yue], Zhang, L.M.[Ling-Ming], Liu, Y.[Yang], Meng, D.Y.[De-Yu], Cui, Z.M.[Zhi-Ming], Gao, C.Q.[Chen-Qiang], Gao, X.B.[Xin-Bo], Lian, C.F.[Chun-Feng], Shen, D.G.[Ding-Gang],
Two-Stream Graph Convolutional Network for Intra-Oral Scanner Image Segmentation,
MedImg(41), No. 4, April 2022, pp. 826-835.
IEEE DOI 2204
BibRef
Earlier: A2, A1, A4, A5, A6, A7, A8, A9, Only:
TSGCNet: Discriminative Geometric Feature Learning with Two-Stream Graph Convolutional Network for 3D Dental Model Segmentation,
CVPR21(6695-6704)
IEEE DOI 2111
Image segmentation, Teeth, Shape, Task analysis, Dentistry, Feature extraction, Intra-oral scanner image segmentation, graph convolutional network. Solid modeling, Surgery, Predictive models BibRef

Gao, Y.[Yue], Zhang, Z.Z.[Zi-Zhao], Lin, H.J.[Hao-Jie], Zhao, X.B.[Xi-Bin], Du, S.Y.[Shao-Yi], Zou, C.Q.[Chang-Qing],
Hypergraph Learning: Methods and Practices,
PAMI(44), No. 5, May 2022, pp. 2548-2566.
IEEE DOI 2204
Learning systems, Correlation, Data models, Laplace equations, Brain modeling, Task analysis, Hypergraph learning, classification and clustering BibRef

Tinega, H.C.[Haron C.], Chen, E.[Enqing], Ma, L.[Long], Nyasaka, D.O.[Divinah O.], Mariita, R.M.[Richard M.],
HybridGBN-SR: A Deep 3D/2D Genome Graph-Based Network for Hyperspectral Image Classification,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Yao, K.X.[Kai-Xuan], Liang, J.[Jiye], Liang, J.Q.[Jian-Qing], Li, M.[Ming], Cao, F.L.[Fei-Long],
Multi-view graph convolutional networks with attention mechanism,
AI(307), 2022, pp. 103708.
Elsevier DOI 2204
Graph neural networks, Multi-view learning, Attention mechanism, Semi-supervised learning BibRef

Zhang, L.[Li], Song, H.[Heda], Aletras, N.[Nikolaos], Lu, H.P.[Hai-Ping],
Node-Feature Convolution for Graph Convolutional Networks,
PR(128), 2022, pp. 108661.
Elsevier DOI 2205
Graph, Representation learning, Graph convolutional networks, Convolutional neural networks BibRef

Liang, H.J.[Hao-Jian], Wang, S.H.[Shao-Hua], Li, H.[Huilai], Ye, H.[Huichun], Zhong, Y.[Yang],
A Trade-Off Algorithm for Solving p-Center Problems with a Graph Convolutional Network,
IJGI(11), No. 5, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Bianchi, F.M.[Filippo Maria], Grattarola, D.[Daniele], Livi, L.[Lorenzo], Alippi, C.[Cesare],
Graph Neural Networks With Convolutional ARMA Filters,
PAMI(44), No. 7, July 2022, pp. 3496-3507.
IEEE DOI 2206
Convolution, Laplace equations, Task analysis, Graph neural networks, Chebyshev approximation, graph signal processing BibRef

Jing, H.Y.[Hao-Yu], Wang, Y.Y.[Yuan-Yuan], Du, Z.H.[Zhen-Hong], Zhang, F.[Feng],
Hyperspectral Image Classification with a Multiscale Fusion-Evolution Graph Convolutional Network Based on a Feature-Spatial Attention Mechanism,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Huang, Y.[Yan], Zhou, X.[Xiao], Xi, B.[Bobo], Li, J.J.[Jiao-Jiao], Kang, J.[Jian], Tang, S.[Shiyang], Chen, Z.[Zhanye], Hong, W.[Wei],
Diverse-Region Hyperspectral Image Classification via Superpixelwise Graph Convolution Technique,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Jiang, B.[Bo], Wang, B.B.[Bei-Bei], Tang, J.[Jin], Luo, B.[Bin],
GeCNs: Graph Elastic Convolutional Networks for Data Representation,
PAMI(44), No. 9, September 2022, pp. 4935-4947.
IEEE DOI 2208
Convolution, Task analysis, Optimization, Supervised learning, Machine learning, Training, Sparse matrices, graph representation BibRef

Zhao, Z.N.[Zhi-Neng], Liu, Q.F.[Qi-Fan], Cao, W.M.[Wen-Ming], Lian, D.L.[De-Liang], He, Z.H.[Zhi-Hai],
Self-guided information for few-shot classification,
PR(131), 2022, pp. 108880.
Elsevier DOI 2208
Few-shot classification, Graph convolution network, Self-guided information BibRef

Guan, W.[Weili], Wen, H.K.[Hao-Kun], Song, X.M.[Xue-Meng], Wang, C.[Chun], Yeh, C.H.[Chung-Hsing], Chang, X.J.[Xiao-Jun], Nie, L.Q.[Li-Qiang],
Partially Supervised Compatibility Modeling,
IP(31), 2022, pp. 4733-4745.
IEEE DOI 2208
Visualization, Representation learning, Graph neural networks, Task analysis, Semantics, Image color analysis, Data models, graph convolutional network BibRef

Duan, Y.J.[Yi-Jun], Liu, X.[Xin], Jatowt, A.[Adam], Yu, H.T.[Hai-Tao], Lynden, S.[Steven], Kim, K.S.[Kyoung-Sook], Matono, A.[Akiyoshi],
Long-Tailed Graph Representation Learning via Dual Cost-Sensitive Graph Convolutional Network,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Duan, Y.J.[Yi-Jun], Liu, X.[Xin], Jatowt, A.[Adam], Yu, H.T.[Hai-Tao], Lynden, S.[Steven], Kim, K.S.[Kyoung-Sook], Matono, A.[Akiyoshi],
SORAG: Synthetic Data Over-Sampling Strategy on Multi-Label Graphs,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Ma, X.Q.[Xue-Qi], Liu, W.F.[Wei-Feng], Tian, Q.[Qi], Gao, Y.[Yue],
Learning Representation on Optimized High-Order Manifold for Visual Classification,
MultMed(24), 2022, pp. 3989-4001.
IEEE DOI 2208
Manifolds, Laplace equations, Correlation, Task analysis, Visualization, Shape, High-order manifold, hypergraph, visual classification BibRef

Liu, X.[Xue], Sun, D.[Dan], Wei, W.[Wei],
Alleviating the over-smoothing of graph neural computing by a data augmentation strategy with entropy preservation,
PR(132), 2022, pp. 108951.
Elsevier DOI 2209
Graph representation, Graph convolutional networks, Information theory, Graph entropy BibRef

Priebe, C.E.[Carey E.], Shen, C.[Cencheng], Huang, N.[Ningyuan], Chen, T.Y.[Tian-Yi],
A Simple Spectral Failure Mode for Graph Convolutional Networks,
PAMI(44), No. 11, November 2022, pp. 8689-8693.
IEEE DOI 2210
Convolution, Task analysis, Symmetric matrices, Graph neural networks, Geometry, Training, Training data, convolutional neural network BibRef

Zhang, R.[Rui], Zhang, W.[Wenlin], Li, P.[Pei], Li, X.L.[Xue-Long],
Graph Convolution RPCA With Adaptive Graph,
IP(31), 2022, pp. 6062-6071.
IEEE DOI 2210
Principal component analysis, Matrix decomposition, Manifolds, Sparse matrices, Convolution, Image reconstruction, Robustness, graph auto-encoder BibRef

Lei, B.Y.[Bai-Ying], Zhu, Y.[Yun], Yu, S.Z.[Shuang-Zhi], Hu, H.[Huoyou], Xu, Y.[Yanwu], Yue, G.H.[Guang-Hui], Wang, T.F.[Tian-Fu], Zhao, C.[Cheng], Chen, S.[Shaobin], Yang, P.[Peng], Song, X.G.[Xue-Gang], Xiao, X.H.[Xiao-Hua], Wang, S.Q.[Shu-Qiang],
Multi-scale enhanced graph convolutional network for mild cognitive impairment detection,
PR(134), 2023, pp. 109106.
Elsevier DOI 2212
Mild cognitive impairment detection, Multimodal brain connectivity networks, Multi-scale enhanced graph convolutional network BibRef

Cao, C.Q.[Cong-Qi], Zhang, X.[Xin], Zhang, S.Z.[Shi-Zhou], Wang, P.[Peng], Zhang, Y.N.[Yan-Ning],
Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos,
SPLetters(29), 2022, pp. 2497-2501.
IEEE DOI 2212
Videos, Feature extraction, Adaptation models, Training, Context modeling, Convolution, Anomaly detection, temporal modeling BibRef

Zhang, S.X.[Shao-Xuan], Feng, J.[Jian], Lu, S.[Senxiang],
A novel method for fusing graph convolutional network and feature based on feedback connection mechanism for nondestructive testing,
PRL(164), 2022, pp. 284-292.
Elsevier DOI 2212
BibRef

Chen, D.W.[Dong-Wen], Qing, C.M.[Chun-Mei], Lin, X.[Xu], Ye, M.T.[Meng-Tao], Xu, X.M.[Xiang-Min], Dickinson, P.[Patrick],
Intra- and Inter-Reasoning Graph Convolutional Network for Saliency Prediction on 360° Images,
CirSysVideo(32), No. 12, December 2022, pp. 8730-8743.
IEEE DOI 2212
Feature extraction, Semantics, Convolution, Predictive models, Image edge detection, Distortion, Data mining, Virtual reality, graph convolutional network BibRef

Wei, F.F.[Fei-Fei], Ping, M.Z.[Ming-Zhu], Mei, K.Z.[Kui-Zhi],
Structure-based graph convolutional networks with frequency filter,
PRL(164), 2022, pp. 161-165.
Elsevier DOI 2212
Network representation learning, Node embeddings, Graph filtering, Graph convolution neural network BibRef

Zeng, X.[Xuhui], Wang, S.[Shu], Zhu, Y.Q.[Yun-Qiang], Xu, M.F.[Meng-Fei], Zou, Z.Q.[Zhi-Qiang],
A Knowledge Graph Convolutional Networks Method for Countryside Ecological Patterns Recommendation by Mining Geographical Features,
IJGI(11), No. 12, 2022, pp. xx-yy.
DOI Link 2301
BibRef

Kazi, A.[Anees], Cosmo, L.[Luca], Ahmadi, S.A.[Seyed-Ahmad], Navab, N.[Nassir], Bronstein, M.M.[Michael M.],
Differentiable Graph Module (DGM) for Graph Convolutional Networks,
PAMI(45), No. 2, February 2023, pp. 1606-1617.
IEEE DOI 2301
Computational modeling, Task analysis, Convolution, Training, Topology, Pipelines, disease prediction BibRef

Wang, X.[Xili], Liang, Z.Y.[Zheng-Yin],
Hybrid network model based on 3D convolutional neural network and scalable graph convolutional network for hyperspectral image classification,
IET-IPR(17), No. 1, 2023, pp. 256-273.
DOI Link 2301
BibRef

Valem, L.P.[Lucas Pascotti], Guimarães-Pedronette, D.C.[Daniel Carlos], Latecki, L.J.[Longin Jan],
Graph Convolutional Networks based on manifold learning for semi-supervised image classification,
CVIU(227), 2023, pp. 103618.
Elsevier DOI 2301
Manifold learning, Graph Convolutional Networks, Image classification, Semi-supervised BibRef

Wu, F.[Fei], Li, S.[Shuaishuai], Gao, G.[Guangwei], Ji, Y.[Yimu], Jing, X.Y.[Xiao-Yuan], Wan, Z.G.[Zhi-Guo],
Semi-supervised cross-modal hashing via modality-specific and cross-modal graph convolutional networks,
PR(136), 2023, pp. 109211.
Elsevier DOI 2301
Cross-modal hashing, semi-supervised learning, graph convolutional networks, modality-specific features, modality-shared features BibRef

Sellami, A.[Akrem], Farah, M.[Mohamed], Dalla-Mura, M.[Mauro],
SHCNet: A semi-supervised hypergraph convolutional networks based on relevant feature selection for hyperspectral image classification,
PRL(165), 2023, pp. 98-106.
Elsevier DOI 2301
Unsupervised feature selection, Hypergraph convolutional network, Dimensionality reduction BibRef

Huang, C.Q.[Chang-Qin], Li, M.[Ming], Cao, F.L.[Fei-Long], Fujita, H.[Hamido], Li, Z.[Zhao], Wu, X.D.[Xin-Dong],
Are Graph Convolutional Networks With Random Weights Feasible?,
PAMI(45), No. 3, March 2023, pp. 2751-2768.
IEEE DOI 2302
Training, Analytical models, Upper bound, Stability analysis, Neural networks, Convolution, Convolutional neural networks, approximation upper bound BibRef

Nikolentzos, G.[Giannis], Dasoulas, G.[George], Vazirgiannis, M.[Michalis],
Permute Me Softly: Learning Soft Permutations for Graph Representations,
PAMI(45), No. 4, April 2023, pp. 5087-5098.
IEEE DOI 2303
Biological system modeling, Computational modeling, Stochastic processes, Message passing, graph representations BibRef

Stankovic´, L.[Ljubiša], Mandic, D.P.[Danilo P.],
Understanding the Basis of Graph Convolutional Neural Networks via an Intuitive Matched Filtering Approach,
SPMag(40), No. 2, March 2023, pp. 155-165.
IEEE DOI 2303
Lecture Notes. Matched filters, Closed box, Convolutional neural networks, Graph neural networks BibRef

Xiao, H.L.[Hao-Liang], Chen, X.Y.[Xiang-Yang],
Drug ADMET Prediction Method Based on Improved Graph Convolution Neural Network,
ICRVC22(266-271)
IEEE DOI 2301
Drugs, Toxicology, Machine learning algorithms, Convolution, Computational modeling, Biological system modeling, Attention mechanism BibRef

Singh, I.P.[Inder Pal], Ghorbel, E.[Enjie], Oyedotun, O.[Oyebade], Aouada, D.[Djamila],
Multi Label Image Classification using Adaptive Graph Convolutional Networks (ML-AGCN),
ICIP22(1806-1810)
IEEE DOI 2211
Knowledge engineering, Adaptation models, Adaptive systems, Correlation, Network topology, Convolution, Image processing BibRef

Xu, J.Y.[Jia-Yi], Yang, Q.[Qin], Li, C.[Chenglin], Zou, J.[Junni], Xiong, H.K.[Hong-Kai], Pan, X.L.[Xin-Long], Wang, H.[Haipeng],
Rotation-Equivariant Graph Convolutional Networks For Spherical Data Via Global-Local Attention,
ICIP22(2501-2505)
IEEE DOI 2211
Correlation, Convolution, Data processing, Topology, Computational efficiency, Kernel, Task analysis, Spherical images, semantic segmentation BibRef

Mostafa, A.[Abdelrahman], Peng, W.[Wei], Zhao, G.Y.[Guo-Ying],
Hyperbolic Spatial Temporal Graph Convolutional Networks,
ICIP22(3301-3305)
IEEE DOI 2211
Representation learning, Geometry, Image recognition, Distortion, Hyperbolic geometry, dynamic graphs, human action recognition BibRef

Guan, Y.H.[Yong-Hang], Zhang, J.[Jun], Tian, K.[Kuan], Yang, S.[Sen], Dong, P.[Pei], Xiang, J.X.[Jin-Xi], Yang, W.[Wei], Huang, J.Z.[Jun-Zhou], Zhang, Y.Y.[Yu-Yao], Han, X.[Xiao],
Node-aligned Graph Convolutional Network for Whole-slide Image Representation and Classification,
CVPR22(18791-18801)
IEEE DOI 2210
Convolutional codes, Pathology, Correlation, Convolution, Computational modeling, Image representation, Medical, Self- semi- meta- unsupervised learning BibRef

Yu, Z.J.[Zi-Jian], Li, X.[Xuhui], Huang, H.J.[Hui-Juan], Zheng, W.[Wen], Chen, L.[Li],
Cascade Image Matting with Deformable Graph Refinement,
ICCV21(7147-7156)
IEEE DOI 2203
Image resolution, Estimation, Graph neural networks, Convolutional neural networks, BibRef

Li, Y.[Yao], Fu, X.[Xueyang], Zha, Z.J.[Zheng-Jun],
Cross-Patch Graph Convolutional Network for Image Denoising,
ICCV21(4631-4640)
IEEE DOI 2203
Training, Image resolution, Pipelines, Noise reduction, Training data, Robustness, Hardware, Computational photography BibRef

Huang, H.M.[Hui-Min], Lin, L.[Lanfen], Zhang, Y.[Yue], Xu, Y.Y.[Ying-Ying], Zheng, J.[Jing], Mao, X.W.[Xiong-Wei], Qian, X.H.[Xiao-Han], Peng, Z.[Zhiyi], Zhou, J.Y.[Jian-Ying], Chen, Y.W.[Yen-Wei], Tong, R.F.[Ruo-Feng],
Graph-BAS3Net: Boundary-Aware Semi-Supervised Segmentation Network with Bilateral Graph Convolution,
ICCV21(7366-7375)
IEEE DOI 2203
Image segmentation, Shape, Convolution, Semantics, Semisupervised learning, Multitasking, Feature extraction, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Sofianos, T.[Theodoros], Sampieri, A.[Alessio], Franco, L.[Luca], Galasso, F.[Fabio],
Space-Time-Separable Graph Convolutional Network for Pose Forecasting,
ICCV21(11189-11198)
IEEE DOI 2203
Convolutional codes, Correlation, Computational modeling, Time series analysis, Dynamics, Kinematics, Gestures and body pose, Motion and tracking BibRef

Dang, L.W.[Ling-Wei], Nie, Y.W.[Yong-Wei], Long, C.J.[Cheng-Jiang], Zhang, Q.[Qing], Li, G.Q.[Gui-Qing],
MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction,
ICCV21(11447-11456)
IEEE DOI 2203
Convolutional codes, Convolution, Dynamics, Benchmark testing, Feature extraction, History, Gestures and body pose, Machine learning architectures and formulations BibRef

Sahbi, H.[Hichem],
Learning Laplacians in Chebyshev Graph Convolutional Networks,
DLGC21(2064-2075)
IEEE DOI 2112
Training, Laplace equations, Convolution, Databases, Neural networks, Chebyshev approximation, Skeleton BibRef

Potter, K.[Kevin], Sleder, S.[Steven], Smith, M.[Matthew], Perera, S.[Shehan], Yilmaz, A.[Alper], Tencer, J.[John],
Parameterized Pseudo-Differential Operators for Graph Convolutional Neural Networks,
GSP-CV21(904-912)
IEEE DOI 2112
Image segmentation, Image coding, Shape, Convolution, Image edge detection, Supervised learning BibRef

Caramalau, R.[Razvan], Bhattarai, B.[Binod], Kim, T.K.[Tae-Kyun],
Sequential Graph Convolutional Network for Active Learning,
CVPR21(9578-9587)
IEEE DOI 2111
Convolution, Image edge detection, Pose estimation, Benchmark testing, Pattern recognition, Task analysis BibRef

Yang, X.[Xu], Deng, C.[Cheng], Dang, Z.Y.[Zhi-Yuan], Wei, K.[Kun], Yan, J.C.[Jun-Chi],
Self-SAGCN: Self-Supervised Semantic Alignment for Graph Convolution Network,
CVPR21(16770-16779)
IEEE DOI 2111
Deep learning, Convolution, Computational modeling, Semantics, Benchmark testing, Feature extraction BibRef

Dai, J.[Jindou], Wu, Y.W.[Yu-Wei], Gao, Z.[Zhi], Jia, Y.D.[Yun-De],
A Hyperbolic-to-Hyperbolic Graph Convolutional Network,
CVPR21(154-163)
IEEE DOI 2111
Manifolds, Geometry, Convolution, Distortion, Pattern recognition, Task analysis BibRef

Wang, J.F.[Jun-Fu], Wang, Y.H.[Yun-Hong], Yang, Z.[Zhen], Yang, L.[Liang], Guo, Y.F.[Yuan-Fang],
Bi-GCN: Binary Graph Convolutional Network,
CVPR21(1561-1570)
IEEE DOI 2111
Memory management, Loading, Graph neural networks, Pattern recognition, Task analysis BibRef

Jing, Y.C.[Yong-Cheng], Yang, Y.D.[Yi-Ding], Wang, X.C.[Xin-Chao], Song, M.L.[Ming-Li], Tao, D.C.[Da-Cheng],
Amalgamating Knowledge from Heterogeneous Graph Neural Networks,
CVPR21(15704-15713)
IEEE DOI 2111
Knowledge engineering, Convolutional codes, Annotations, Semantics, Graph neural networks 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

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

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

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

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

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, Convolutional neural networks 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

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

Litany, O., Bronstein, A.M., Bronstein, M.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 .