14.5.10.7.1 Graph Neural Networks

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

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

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

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

Wang, W.[Wei], Gao, J.Y.[Jun-Yu], Yang, X.S.[Xiao-Shan], 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

Abadal, S.[Sergi], Jain, A.[Akshay], Guirado, R.[Robert], Lopez-Alonso, J.[Jorge], Alarcon, E.[Eduard],
Computing Graph Neural Networks: A Survey from Algorithms to Accelerators,
Surveys(54), No. 9, October 2021, pp. xx-yy.
DOI Link 2112
Survey, Graph Neural Networks. Graph neural networks, GNN algorithms, graph embeddings, accelerators BibRef

Tiezzi, M.[Matteo], Marra, G.[Giuseppe], Melacci, S.[Stefano], Maggini, M.[Marco],
Deep Constraint-Based Propagation in Graph Neural Networks,
PAMI(44), No. 2, February 2022, pp. 727-739.
IEEE DOI 2201
Optimization, Computational modeling, Training, Graph neural networks, Data models, Biological neural networks, lagrangian optimization BibRef

Ciano, G.[Giorgio], Rossi, A.[Alberto], Bianchini, M.[Monica], Scarselli, F.[Franco],
On Inductive-Transductive Learning With Graph Neural Networks,
PAMI(44), No. 2, February 2022, pp. 758-769.
IEEE DOI 2201
Neural networks, Computational modeling, Training, Encoding, Graph neural networks, Topology, Diffusion processes, inductive learning BibRef

Ding, J.Y.[Jing-Yi], Cheng, R.[Ruohui], Song, J.[Jian], Zhang, X.R.[Xiang-Rong], Jiao, L.C.[Li-Cheng], Wu, J.[Jianshe],
Graph label prediction based on local structure characteristics representation,
PR(125), 2022, pp. 108525.
Elsevier DOI 2203
Graph classification, Graph neural network, Betweenness centrality node, Feature fusion, Characteristics representation BibRef

Chen, Y.C.[Yu-Chi], Lai, K.T.[Kuan-Ting], Liu, D.[Dong], Chen, M.S.[Ming-Syan],
TAGNet: Triplet-Attention Graph Networks for Hashtag Recommendation,
CirSysVideo(32), No. 3, March 2022, pp. 1148-1159.
IEEE DOI 2203
Feature extraction, Visualization, Social networking (online), Correlation, Convolution, Fuses, Blogs, Hashtag recommendation, attention mechanism BibRef

Kan, S.C.[Shi-Chao], Cen, Y.G.[Yi-Gang], Li, Y.[Yang], Vladimir, M.[Mladenovic], He, Z.H.[Zhi-Hai],
Local Semantic Correlation Modeling Over Graph Neural Networks for Deep Feature Embedding and Image Retrieval,
IP(31), 2022, pp. 2988-3003.
IEEE DOI 2205
Correlation, Graph neural networks, Measurement, Semantics, Image retrieval, Training, Visualization, Deep feature embedding BibRef

Thang, D.C.[Duong Chi], Dat, H.T.[Hoang Thanh], Tam, N.T.[Nguyen Thanh], Jo, J.[Jun], Hung, N.Q.V.[Nguyen Quoc Viet], Aberer, K.[Karl],
Nature vs. Nurture: Feature vs. Structure for Graph Neural Networks,
PRL(159), 2022, pp. 46-53.
Elsevier DOI 2206
graph neural networks, transferability BibRef

Gao, H.Y.[Hong-Yang], Ji, S.W.[Shui-Wang],
Graph U-Nets,
PAMI(44), No. 9, September 2022, pp. 4948-4960.
IEEE DOI 2208
Task analysis, Topology, Feature extraction, Neural networks, Logic gates, Lattices, Graph neural networks, U-Net BibRef

Wang, R.Z.[Run-Zhong], Yan, J.C.[Jun-Chi], Yang, X.K.[Xiao-Kang],
Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem With Extension to Hypergraph and Multiple-Graph Matching,
PAMI(44), No. 9, September 2022, pp. 5261-5279.
IEEE DOI 2208
Pattern matching, Tensors, Splines (mathematics), Feature extraction, Peer-to-peer computing, Optimization, graph neural networks BibRef

Tian, Y.[Yu], Sun, X.[Xian], Niu, R.G.[Rui-Gang], Yu, H.F.[Hong-Feng], Zhu, Z.C.[Zi-Cong], Wang, P.[Peijin], Fu, K.[Kun],
Fully-weighted HGNN: Learning efficient non-local relations with hypergraph in aerial imagery,
PandRS(191), 2022, pp. 263-276.
Elsevier DOI 2208
Aerial imagery, Hypergraph neural networks, Fully-weighted Hypergraph Neural Network (fully-weighted HGNN), Hypergraph Convolutional Feature Pyramid Networks (hyper-FPN) BibRef

Isufi, E.[Elvin], Gama, F.[Fernando], Ribeiro, A.[Alejandro],
EdgeNets: Edge Varying Graph Neural Networks,
PAMI(44), No. 11, November 2022, pp. 7457-7473.
IEEE DOI 2210
Convolution, Neural networks, Graph neural networks, Computational complexity, Tools, Laplace equations, Edge varying, learning on graphs BibRef

Liu, M.[Meng], Wang, Z.Y.[Zheng-Yang], Ji, S.W.[Shui-Wang],
Non-Local Graph Neural Networks,
PAMI(44), No. 12, December 2022, pp. 10270-10276.
IEEE DOI 2212
Sorting, Task analysis, Graph neural networks, Convolution, Aggregates, Nonhomogeneous media, Calibration, disassortative graphs BibRef

Li, S.[Shuo], Liu, F.[Fang], Jiao, L.C.[Li-Cheng], Chen, P.[Puhua], Liu, X.[Xu], Li, L.L.[Ling-Ling],
MFNet: A Novel GNN-Based Multi-Level Feature Network With Superpixel Priors,
IP(31), 2022, pp. 7306-7321.
IEEE DOI 2212
Feature extraction, Task analysis, Object detection, Image segmentation, Convolution, Graph neural networks, Shape, representation learning BibRef

Bouritsas, G.[Giorgos], Frasca, F.[Fabrizio], Zafeiriou, S.P.[Stefanos P.], Bronstein, M.M.[Michael M.],
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting,
PAMI(45), No. 1, January 2023, pp. 657-668.
IEEE DOI 2212
Orbits, Message passing, Graph neural networks, Color, Social networking (online), Proteins, Histograms, neural network expressivity BibRef

Abdelaziz, I.[Ibrahim], Crouse, M.[Maxwell], Makni, B.[Bassem], Austel, V.[Vernon], Cornelio, C.[Cristina], Ikbal, S.[Shajith], Kapanipathi, P.[Pavan], Makondo, N.[Ndivhuwo], Srinivas, K.[Kavitha], Witbrock, M.[Michael], Fokoue, A.[Achille],
Learning to Guide a Saturation-Based Theorem Prover,
PAMI(45), No. 1, January 2023, pp. 738-751.
IEEE DOI 2212
Standards, Reinforcement learning, Graph neural networks, Feature extraction, Benchmark testing, Search problems, graph neural networks BibRef

Xie, Y.C.[Yao-Chen], Xu, Z.[Zhao], Zhang, J.T.[Jing-Tun], Wang, Z.Y.[Zheng-Yang], Ji, S.W.[Shui-Wang],
Self-Supervised Learning of Graph Neural Networks: A Unified Review,
PAMI(45), No. 2, February 2023, pp. 2412-2429.
IEEE DOI 2301
Task analysis, Predictive models, Data models, Training, Graph neural networks, Mutual information, Head, Deep learning, unsupervised learning BibRef

Chen, T.L.[Tian-Long], Zhou, K.X.[Kai-Xiong], Duan, K.Y.[Ke-Yu], Zheng, W.Q.[Wen-Qing], Wang, P.H.[Pei-Hao], Hu, X.[Xia], Wang, Z.Y.[Zhang-Yang],
Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study,
PAMI(45), No. 3, March 2023, pp. 2769-2781.
IEEE DOI 2302
Training, Benchmark testing, Standards, Peer-to-peer computing, Graph neural networks, Task analysis, Deep graph neural networks, benchmark BibRef

Vasudevan, V.[Varun], Bassenne, M.[Maxime], Islam, M.T.[Md Tauhidul], Xing, L.[Lei],
Image classification using graph neural network and multiscale wavelet superpixels,
PRL(166), 2023, pp. 89-96.
Elsevier DOI 2302
Image classification, GNN, Multiscale superpixel, Wavelet BibRef

Qian, S.S.[Sheng-Sheng], Xue, D.[Dizhan], Fang, Q.[Quan], Xu, C.S.[Chang-Sheng],
Integrating Multi-Label Contrastive Learning With Dual Adversarial Graph Neural Networks for Cross-Modal Retrieval,
PAMI(45), No. 4, April 2023, pp. 4794-4811.
IEEE DOI 2303
Semantics, Correlation, Data models, Task analysis, Graph neural networks, Generative adversarial networks, Training BibRef

Mohamed, H.A.[Hebatallah A.], Pilutti, D.[Diego], James, S.[Stuart], del Bue, A.[Alessio], Pelillo, M.[Marcello], Vascon, S.[Sebastiano],
Locality-aware subgraphs for inductive link prediction in knowledge graphs,
PRL(167), 2023, pp. 90-97.
Elsevier DOI 2303
Knowledge graphs, Inductive link prediction, Graph neural networks, Local clustering, Personalized PageRank BibRef

Kaczmarek, I.[Iwona], Iwaniak, A.[Adam], Swietlicka, A.[Aleksandra],
Classification of Spatial Objects with the Use of Graph Neural Networks,
IJGI(12), No. 3, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Fan, X.L.[Xiao-Long], Gong, M.[Maoguo], Wu, Y.[Yue],
Markov clustering regularized multi-hop graph neural network,
PR(139), 2023, pp. 109518.
Elsevier DOI 2304
Graph data mining, Graph neural network, Graph-level representation learning, Graph pattern recognition BibRef

Hao, Y.J.[Yong-Jing], Ma, J.[Jun], Zhao, P.P.[Peng-Peng], Liu, G.F.[Guan-Feng], Xian, X.F.[Xue-Feng], Zhao, L.[Lei], Sheng, V.S.[Victor S.],
Multi-dimensional Graph Neural Network for Sequential Recommendation,
PR(139), 2023, pp. 109504.
Elsevier DOI 2304
Sequential Recommendation, Graph Neural Networks, Self-attention Networks, Graph Embedding BibRef

Wang, Z.Y.[Zheng-Yang], Ji, S.W.[Shui-Wang],
Second-Order Pooling for Graph Neural Networks,
PAMI(45), No. 6, June 2023, pp. 6870-6880.
IEEE DOI 2305
Neural networks, Task analysis, Deep learning, Correlation, Covariance matrices, Graph neural networks, graph pooling, second-order statistics BibRef

Mueller, T.T.[Tamara T.], Paetzold, J.C.[Johannes C.], Prabhakar, C.[Chinmay], Usynin, D.[Dmitrii], Rueckert, D.[Daniel], Kaissis, G.[Georgios],
Differentially Private Graph Neural Networks for Whole-Graph Classification,
PAMI(45), No. 6, June 2023, pp. 7308-7318.
IEEE DOI 2305
Training, Privacy, Task analysis, Graph neural networks, Data models, Stochastic processes, Image edge detection, Differential privacy, graph neural networks BibRef

Jiang, X.D.[Xiao-Dong], Zhu, R.H.[Rong-Hang], Ji, P.S.[Peng-Sheng], Li, S.[Sheng],
Co-Embedding of Nodes and Edges With Graph Neural Networks,
PAMI(45), No. 6, June 2023, pp. 7075-7086.
IEEE DOI 2305
Task analysis, Convolution, Deep learning, Switches, Image edge detection, Prediction algorithms, Graph embedding, link prediction BibRef

Wan, H.[Hai], Zhang, X.W.[Xin-Wei], Zhang, Y.[Yubo], Zhao, X.[Xibin], Ying, S.[Shihui], Gao, Y.[Yue],
Structure Evolution on Manifold for Graph Learning,
PAMI(45), No. 6, June 2023, pp. 7751-7763.
IEEE DOI 2305
Manifolds, Task analysis, Convolution, Data models, Graph neural networks, Energy measurement, Correlation, graph energy BibRef

Lyu, S.[Shuaiyi], Wang, K.[Kai], Zhang, L.[Liren], Wang, B.L.[Bai-Ling],
Process-Oriented heterogeneous graph learning in GNN-Based ICS anomalous pattern recognition,
PR(141), 2023, pp. 109661.
Elsevier DOI 2306
Fine-Grained anomaly recognition, Process-Oriented associativity, Heterogeneous graph learning, Industrial control systems BibRef

Gillioz, A.[Anthony], Riesen, K.[Kaspar],
Graph Reduction Neural Networks for Structural Pattern Recognition,
SSSPR22(64-73).
Springer DOI 2301
BibRef

Seo, S.[Sangwoo], Jung, S.[Seungjun], Kim, C.[Changick],
Explanation-based Graph Neural Networks for Graph Classification,
ICPR22(2836-2842)
IEEE DOI 2212
Proteins, Analytical models, Machine learning, Benchmark testing, Graph neural networks, Data models BibRef

Wei, Z.[Ziyu], Xiao, X.[Xi], Zhang, B.[Bin], Hu, G.W.[Guang-Wu], Li, Q.[Qing], Xia, S.T.[Shu-Tao],
Graph Data Augmentation for Node Classification,
ICPR22(4899-4905)
IEEE DOI 2212
Computational modeling, Benchmark testing, Graph neural networks, Topology BibRef

Kim, J.[Jinwoo], Oh, S.[Saeyoon], Cho, S.J.[Sung-Jun], Hong, S.[Seunghoon],
Equivariant Hypergraph Neural Networks,
ECCV22(XXI:86-103).
Springer DOI 2211
BibRef

Lin, W.[Wanyu], Lan, H.[Hao], Wang, H.[Hao], Li, B.[Baochun],
OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks,
CVPR22(13719-13728)
IEEE DOI 2210
Visualization, Privacy, Statistical analysis, Semantics, Training data, Medical services, privacy and ethics in vision, Transparency BibRef

Schaefer, S.[Simon], Gehrig, D.[Daniel], Scaramuzza, D.[Davide],
AEGNN: Asynchronous Event-based Graph Neural Networks,
CVPR22(12361-12371)
IEEE DOI 2210
Code, GNN.
WWW Link. Power demand, Object detection, Market research, Graph neural networks, Pattern recognition, Object recognition, Scene analysis and understanding BibRef

Wu, H.Y.[Hong-Yan], Guo, H.Y.[Hai-Yun], Miao, Q.H.[Qing-Hai], Huang, M.[Min], Wang, J.Q.[Jin-Qiao],
Graph Neural Networks Based Multi-granularity Feature Representation Learning for Fine-Grained Visual Categorization,
MMMod22(II:230-242).
Springer DOI 2203
BibRef

Zhao, G.M.[Gang-Ming], Ge, W.F.[Wei-Feng], Yu, Y.Z.[Yi-Zhou],
GraphFPN: Graph Feature Pyramid Network for Object Detection,
ICCV21(2743-2752)
IEEE DOI 2203
Representation learning, Image segmentation, Network topology, Object detection, Feature extraction, Graph neural networks, grouping and shape BibRef

Xing, Y.F.[Yi-Fan], He, T.[Tong], Xiao, T.J.[Tian-Jun], Wang, Y.X.[Yong-Xin], Xiong, Y.J.[Yuan-Jun], Xia, W.[Wei], Wipf, D.[David], Zhang, Z.[Zheng], Soatto, S.[Stefano],
Learning Hierarchical Graph Neural Networks for Image Clustering,
ICCV21(3447-3457)
IEEE DOI 2203
Training, Couplings, Computational modeling, Predictive models, Prediction algorithms, Graph neural networks, Faces, Recognition and classification BibRef

Liu, N.[Nian], Zhao, W.[Wangbo], Zhang, D.W.[Ding-Wen], Han, J.W.[Jun-Wei], Shao, L.[Ling],
Light Field Saliency Detection with Dual Local Graph Learning and Reciprocative Guidance,
ICCV21(4692-4701)
IEEE DOI 2203
Fuses, Convolution, Computational modeling, Object detection, Light fields, Graph neural networks, Scene analysis and understanding BibRef

Wang, T.T.[Tian-Tian], Liu, S.[Sifei], Tian, Y.[Yapeng], Li, K.[Kai], Yang, M.H.[Ming-Hsuan],
Video Matting via Consistency-Regularized Graph Neural Networks,
ICCV21(4882-4891)
IEEE DOI 2203
Training, Adaptation models, Computational modeling, Coherence, Predictive models, Graph neural networks, grouping and shape 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],
Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networks,
ICCV21(5281-5290)
IEEE DOI 2203
Visualization, Adaptation models, Network topology, Computational modeling, Aggregates, Transformers, Vision applications and systems BibRef

Li, X.Y.[Xin-Yi], Ling, H.B.[Hai-Bin],
PoGO-Net: Pose Graph Optimization with Graph Neural Networks,
ICCV21(5875-5885)
IEEE DOI 2203
Training, Simultaneous localization and mapping, Pose estimation, Benchmark testing, Cameras, Robustness, Graph neural networks, Vision for robotics and autonomous vehicles BibRef

Chen, H.K.[Hong-Kai], Luo, Z.X.[Zi-Xin], Zhang, J.H.[Jia-Hui], Zhou, L.[Lei], Bai, X.Y.[Xu-Yang], Hu, Z.[Zeyu], Tai, C.L.[Chiew-Lan], Quan, L.[Long],
Learning to Match Features with Seeded Graph Matching Network,
ICCV21(6281-6290)
IEEE DOI 2203
Costs, Filtering, Message passing, Image matching, Computer network reliability, Graph neural networks, Stereo, Low-level and physics-based vision BibRef

Arnab, A.[Anurag], Sun, C.[Chen], Schmid, C.[Cordelia],
Unified Graph Structured Models for Video Understanding,
ICCV21(8097-8106)
IEEE DOI 2203
Computational modeling, Message passing, Genomics, Cognition, Graph neural networks, Task analysis, Action and behavior recognition BibRef

Fang, P.F.[Peng-Fei], Harandi, M.[Mehrtash], Petersson, L.[Lars],
Kernel Methods in Hyperbolic Spaces,
ICCV21(10645-10654)
IEEE DOI 2203
Geometry, Machine learning, Hilbert space, Natural language processing, Graph neural networks, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Zeng, A.[Ailing], Sun, X.[Xiao], Yang, L.[Lei], Zhao, N.X.[Nan-Xuan], Liu, M.H.[Min-Hao], Xu, Q.[Qiang],
Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimation,
ICCV21(11416-11425)
IEEE DOI 2203
Representation learning, Deep learning, Codes, Pose estimation, Graph neural networks, Gestures and body pose, Representation learning BibRef

Zhang, C.[Cheng], Cui, Z.P.[Zhao-Peng], Chen, C.[Cai], Liu, S.C.[Shuai-Cheng], Zeng, B.[Bing], Bao, H.J.[Hu-Jun], Zhang, Y.[Yinda],
DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization,
ICCV21(12612-12621)
IEEE DOI 2203
Shape, Layout, Semantics, Predictive models, Linear programming, Graph neural networks, 3D from a single image and shape-from-x, Detection and localization in 2D and 3D BibRef

Yew, Z.J.[Zi Jian], Lee, G.H.[Gim Hee],
Learning Iterative Robust Transformation Synchronization,
3DV21(1206-1215)
IEEE DOI 2201
Analytical models, Message passing, Pipelines, Graph neural networks, Synchronization, Iterative methods, registration BibRef

Bahri, M.[Mehdi], Bahl, G.[Gaétan], Zafeiriou, S.P.[Stefanos P.],
Binary Graph Neural Networks,
CVPR21(9487-9496)
IEEE DOI 2111
Training, Schedules, Heuristic algorithms, Computational modeling, Memory management, Process control, Benchmark testing BibRef

Miyata, M.[Masaki], Shiraki, K.[Katsutoshi], Minoura, H.[Hiroaki], Hirakawa, T.[Tsubasa], Yamashita, T.[Takayoshi], Fujiyoshi, H.[Hironobu],
Relational Subgraph for Graph-based Path Prediction,
MVA21(1-5)
DOI Link 2109
Prediction methods, Feature extraction 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

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

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

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

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