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.,
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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
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
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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.H.[Ruo-Hui],
Song, J.[Jian],
Zhang, X.R.[Xiang-Rong],
Jiao, L.C.[Li-Cheng],
Wu, J.S.[Jian-She],
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
Sabbaqi, M.[Mohammad],
Isufi, E.[Elvin],
Graph-Time Convolutional Neural Networks:
Architecture and Theoretical Analysis,
PAMI(45), No. 12, December 2023, pp. 14625-14638.
IEEE DOI
2311
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
Gui, S.R.[Shu-Rui],
Yuan, H.[Hao],
Wang, J.[Jie],
Lao, Q.C.[Qi-Cheng],
Li, K.[Kang],
Ji, S.W.[Shui-Wang],
FlowX: Towards Explainable Graph Neural Networks via Message Flows,
PAMI(46), No. 7, July 2024, pp. 4567-4578.
IEEE DOI
2406
Graph neural networks, Task analysis, Predictive models, Training,
Philosophical considerations, Mutual information, Deep learning,
message passing neural networks
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.B.[Xi-Bin],
Ying, S.H.[Shi-Hui],
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
Li, J.X.[Jian-Xin],
Sun, Q.Y.[Qing-Yun],
Peng, H.[Hao],
Yang, B.[Beining],
Wu, J.[Jia],
Yu, P.S.[Philip S.],
Adaptive Subgraph Neural Network With Reinforced Critical Structure
Mining,
PAMI(45), No. 7, July 2023, pp. 8063-8080.
IEEE DOI
2306
Task analysis, Representation learning, Kernel, Shape,
Graph neural networks, Feature extraction, Annotations, mutual information
BibRef
Ansarizadeh, F.[Fatemeh],
Tay, D.B.[David B.],
Thiruvady, D.[Dhananjay],
Robles-kelly, A.[Antonio],
Deterministic sampling in heterogeneous graph neural networks,
PRL(172), 2023, pp. 74-81.
Elsevier DOI
2309
Deterministic sampling, Graph representation learning,
Heterogeneous graph, Katz centrality measure, Network embedding
BibRef
Melton, J.[Joshua],
Krishnan, S.[Siddharth],
muxGNN: Multiplex Graph Neural Network for Heterogeneous Graphs,
PAMI(45), No. 9, September 2023, pp. 11067-11078.
IEEE DOI
2309
BibRef
Jiang, X.Y.[Xing-Yu],
Zhang, S.H.[Shi-Hua],
Zhang, X.P.[Xiao-Ping],
Ma, J.Y.[Jia-Yi],
Improving sparse graph attention for feature matching by informative
keypoints exploration,
CVIU(235), 2023, pp. 103803.
Elsevier DOI
2310
Feature matching, Graph neural network, Outlier, Two-view geometry
BibRef
Bai, N.[Nan],
Nourian, P.[Pirouz],
Luo, R.[Renqian],
Cheng, T.[Tao],
Pereira-Roders, A.[Ana],
Screening the stones of Venice: Mapping social perceptions of
cultural significance through graph-based semi-supervised
classification,
PandRS(203), 2023, pp. 135-164.
Elsevier DOI
2310
Social media data, Multi-modal machine learning,
Graph Neural Networks, Spectral centrality, Label diffusion
BibRef
Wang, B.B.[Bei-Bei],
Jiang, B.[Bo],
Tang, J.[Jin],
Luo, B.[Bin],
Generalizing Aggregation Functions in GNNs:
Building High Capacity and Robust GNNs via Nonlinear Aggregation,
PAMI(45), No. 11, November 2023, pp. 13454-13466.
IEEE DOI
2310
BibRef
Xie, Y.[Yu],
Liang, Y.F.[Yan-Feng],
Gong, M.[Maoguo],
Qin, A.K.,
Ong, Y.S.[Yew-Soon],
He, T.T.[Tian-Tian],
Semisupervised Graph Neural Networks for Graph Classification,
Cyber(53), No. 10, October 2023, pp. 6222-6235.
IEEE DOI
2310
BibRef
Chen, X.[Xu],
Pan, Y.[Yuangang],
Tsang, I.[Ivor],
Zhang, Y.[Ya],
Learning node representations against perturbations,
PR(145), 2024, pp. 109976.
Elsevier DOI Code:
WWW Link.
2311
Graph neural networks, Node representation learning, , ,
BibRef
Lin, H.Y.[Hai-Yang],
Yan, M.Y.[Ming-Yu],
Ye, X.C.[Xiao-Chun],
Fan, D.[Dongrui],
Pan, S.R.[Shi-Rui],
Chen, W.G.[Wen-Guang],
Xie, Y.[Yuan],
A Comprehensive Survey on Distributed Training of Graph Neural
Networks,
PIEEE(111), No. 12, December 2023, pp. 1572-1606.
IEEE DOI
2312
BibRef
Xiao, S.X.[Shun-Xin],
Du, S.[Shide],
Chen, Z.L.[Zhao-Liang],
Zhang, Y.H.[Yun-He],
Wang, S.P.[Shi-Ping],
Dual Fusion-Propagation Graph Neural Network for Multi-View
Clustering,
MultMed(25), 2023, pp. 9203-9215.
IEEE DOI
2312
BibRef
Fan, S.H.[Shao-Hua],
Wang, X.[Xiao],
Shi, C.[Chuan],
Cui, P.[Peng],
Wang, B.[Bai],
Generalizing Graph Neural Networks on Out-of-Distribution Graphs,
PAMI(46), No. 1, January 2024, pp. 322-337.
IEEE DOI
2312
BibRef
Zheng, S.[Shuai],
Zhu, Z.F.[Zhen-Feng],
Liu, Z.[Zhizhe],
Li, Y.[Youru],
Zhao, Y.[Yao],
Node-Oriented Spectral Filtering for Graph Neural Networks,
PAMI(46), No. 1, January 2024, pp. 388-402.
IEEE DOI
2312
BibRef
Zhou, Y.F.[Yi-Fan],
Yu, L.[Lei],
Few-shot learning via weighted prototypes from graph structure,
PRL(176), 2023, pp. 230-235.
Elsevier DOI
2312
Few-shot learning, Prototype network, Prototype modification,
Graph neural networks
BibRef
Wu, H.[Hanrui],
Yip, A.[Andy],
Long, J.Y.[Jin-Yi],
Zhang, J.[Jia],
Ng, M.K.[Michael K.],
Simplicial Complex Neural Networks,
PAMI(46), No. 1, January 2024, pp. 561-575.
IEEE DOI
2312
Block matrices, edge classification, generalization error,
graph learning networks, high-order simplex classification,
node classification, simplicial complex
BibRef
Deb, S.[Swakshar],
Rahman, S.[Shafin],
Rahman, S.[Sejuti],
GA-GWNN: Generalized Adaptive Graph Wavelet Neural Network,
PRL(177), 2024, pp. 128-134.
Elsevier DOI
2401
Graph neural network, Wavelet, Lifting scheme, Bipartite graph,
Node classification
BibRef
Bicciato, A.[Alessandro],
Cosmo, L.[Luca],
Minello, G.[Giorgia],
Rossi, L.[Luca],
Torsello, A.[Andrea],
GNN-LoFI: A novel graph neural network through localized
feature-based histogram intersection,
PR(148), 2024, pp. 110210.
Elsevier DOI
2402
Graph neural network, Deep learning
BibRef
Chen, Z.Q.[Zhi-Qian],
Chen, F.L.[Fang-Lan],
Zhang, L.[Lei],
Ji, T.[Taoran],
Fu, K.[Kaiqun],
Zhao, L.[Liang],
Chen, F.[Feng],
Wu, L.F.[Ling-Fei],
Aggarwal, C.[Charu],
Lu, C.T.[Chang-Tien],
Bridging the Gap between Spatial and Spectral Domains:
A Unified Framework for Graph Neural Networks,
Surveys(56), No. 5, December 2023, pp. xx-yy.
DOI Link
2402
graph learning, spectral graph theory, approximation theory,
graph neural networks, Deep learning
BibRef
Chai, L.[Lang],
Tu, L.[Lilan],
Wang, X.J.[Xian-Jia],
Su, Q.Q.[Qing-Qing],
Hypergraph modeling and hypergraph multi-view attention neural
network for link prediction,
PR(149), 2024, pp. 110292.
Elsevier DOI
2403
Link prediction, Network structure representation,
Hypergraph modeling, Hypergraph learning, Hypergraph neural network
BibRef
Wang, K.[Kun],
Liang, Y.X.[Yu-Xuan],
Li, X.[Xinglin],
Li, G.H.[Guo-Hao],
Ghanem, B.[Bernard],
Zimmermann, R.[Roger],
Zhou, Z.Y.[Zheng-Yang],
Yi, H.[Huahui],
Zhang, Y.D.[Yu-Dong],
Wang, Y.[Yang],
Brave the Wind and the Waves:
Discovering Robust and Generalizable Graph Lottery Tickets,
PAMI(46), No. 5, May 2024, pp. 3388-3405.
IEEE DOI
2404
Training, Robustness, Computational modeling,
Graph neural networks, Streams, out-of-distribution generalization
BibRef
Bontempo, G.[Gianpaolo],
Bolelli, F.[Federico],
Porrello, A.[Angelo],
Calderara, S.[Simone],
Ficarra, E.[Elisa],
A Graph-Based Multi-Scale Approach With Knowledge Distillation for
WSI Classification,
MedImg(43), No. 4, April 2024, pp. 1412-1421.
IEEE DOI
2404
Feature extraction, Proposals, Spatial resolution, Knowledge engineering,
Graph neural networks, Transformers, weakly supervised learning
BibRef
Besta, M.[Maciej],
Hoefler, T.[Torsten],
Parallel and Distributed Graph Neural Networks:
An In-Depth Concurrency Analysis,
PAMI(46), No. 5, May 2024, pp. 2584-2606.
IEEE DOI
2404
Graph neural networks, Vectors, Computational modeling,
Task analysis, Training, Pipeline processing, Taxonomy,
parallel algorithms
BibRef
Huang, Z.Y.[Zhong-Yu],
Wang, Y.H.[Ying-Heng],
Li, C.Z.[Chao-Zhuo],
He, H.G.[Hui-Guang],
Growing Like a Tree: Finding Trunks From Graph Skeleton Trees,
PAMI(46), No. 5, May 2024, pp. 2838-2851.
IEEE DOI
2404
Task analysis, Skeleton, Probabilistic logic, Junctions,
Graph neural networks, Smoothing methods, Graphical models, trunks
BibRef
Ding, S.F.[Shi-Fei],
Du, W.[Wei],
Ding, L.[Ling],
Zhang, J.[Jian],
Guo, L.[Lili],
An, B.[Bo],
Robust Multi-Agent Communication With Graph Information Bottleneck
Optimization,
PAMI(46), No. 5, May 2024, pp. 3096-3107.
IEEE DOI
2404
Task analysis, Perturbation methods, Graph neural networks,
Optimization, Representation learning, Reinforcement learning,
communication learning
BibRef
Ying, Z.J.[Zi-Jian],
Zhang, J.[Jing],
Li, Q.[Qianmu],
Wu, M.[Ming],
Sheng, V.S.[Victor S.],
A Little Truth Injection But a Big Reward:
Label Aggregation With Graph Neural Networks,
PAMI(46), No. 5, May 2024, pp. 3169-3182.
IEEE DOI
2404
Correlation, Task analysis, Annotations, Labeling, Convolution,
Crowdsourcing, Probabilistic logic, Crowdsourcing, truth inference,
interpretable learning
BibRef
Huang, Y.X.[Yi-Xiang],
Hao, H.Y.[Hong-Yu],
Ge, W.C.[Wei-Chao],
Cao, Y.[Yang],
Wu, M.[Ming],
Zhang, C.[Chuang],
Guo, J.[Jun],
Relation fusion propagation network for transductive few-shot
learning,
PR(151), 2024, pp. 110367.
Elsevier DOI
2404
Few-shot learning, Image classification, Graph neural network
BibRef
Shao, M.Q.[Meng-Qiu],
Xue, P.[Peng],
Zhou, X.[Xi],
Shen, X.[Xiao],
Contrastive domain-adaptive graph selective self-training network for
cross-network edge classification,
PR(152), 2024, pp. 110448.
Elsevier DOI
2405
Graph neural network, Cross-network edge classification,
Graph Domain Adaptation, Pseudo-labeling
BibRef
Jiang, M.Y.[Meng-Ying],
Liu, G.Z.[Gui-Zhong],
Su, Y.C.[Yuan-Chao],
Wu, X.L.[Xin-Liang],
Self-attention empowered graph convolutional network for structure
learning and node embedding,
PR(153), 2024, pp. 110537.
Elsevier DOI Code:
WWW Link.
2405
Representation learning, Heterophily, Structure learning, Graph neural networks
BibRef
Yao, H.Y.[Hong-Yu],
Zhang, C.Y.[Chun-Yang],
Yao, Z.L.[Zhi-Liang],
Chen, C.L.P.[C.L. Philip],
Hu, J.F.[Jun-Feng],
A recurrent graph neural network for inductive representation
learning on dynamic graphs,
PR(154), 2024, pp. 110577.
Elsevier DOI
2406
Recurrent graph neural networks, Dynamic networks,
Graph representation learning, Inductive learning, Unsupervised learning
BibRef
Chen, W.[Wei],
Yan, W.X.[Wen-Xu],
Wang, W.Y.[Wen-Yuan],
Adaptive propagation deep graph neural networks,
PR(154), 2024, pp. 110607.
Elsevier DOI Code:
WWW Link.
2406
Graph neural network, Adaptive propagation combinations,
Subjective and objective information, Aggregation weights, Computational costs
BibRef
Deng, S.C.[Su-Cheng],
Yang, G.[Geping],
Yang, Y.Y.[Yi-Yang],
Gong, Z.G.[Zhi-Guo],
Chen, C.[Can],
Chen, X.[Xiang],
Hao, Z.F.[Zhi-Feng],
Module-based graph pooling for graph classification,
PR(154), 2024, pp. 110606.
Elsevier DOI
2406
Graph neural network, Graph classification, Graph pooling
BibRef
Yeom, J.[Jeyoon],
Kim, T.[Taero],
Chang, R.[Rakwoo],
Song, K.[Kyungwoo],
Structural and positional ensembled encoding for Graph Transformer,
PRL(183), 2024, pp. 104-110.
Elsevier DOI
2406
Graph neural network, Graph Transformer, Positional encoding,
Graph clustering, Attention
BibRef
Song, L.X.[Lin-Xuan],
Tu, W.X.[Wen-Xuan],
Zhou, S.H.[Si-Hang],
Zhu, E.[En],
GANN: Graph Alignment Neural Network for semi-supervised learning,
PR(154), 2024, pp. 110484.
Elsevier DOI
2406
Attributes mining, High-order neighborhood exploration,
Semi-supervised node classification, Graph neural networks
BibRef
Luo, D.S.[Dong-Sheng],
Zhao, T.X.[Tian-Xiang],
Cheng, W.[Wei],
Xu, D.[Dongkuan],
Han, F.[Feng],
Yu, W.C.[Wen-Chao],
Liu, X.[Xiao],
Chen, H.F.[Hai-Feng],
Zhang, X.[Xiang],
Towards Inductive and Efficient Explanations for Graph Neural
Networks,
PAMI(46), No. 8, August 2024, pp. 5245-5259.
IEEE DOI
2407
Graph neural networks, Predictive models, Task analysis,
Computational modeling, Mutual information, Feature extraction,
interpretability
BibRef
Shen, D.[Danyao],
Hu, H.J.[Hao-Jie],
He, F.[Fang],
Zhang, F.G.[Feng-Gan],
Zhao, J.W.[Jian-Wei],
Shen, X.W.[Xiao-Wei],
Hierarchical Prototype-Aligned Graph Neural Network for Cross-Scene
Hyperspectral Image Classification,
RS(16), No. 13, 2024, pp. 2464.
DOI Link
2407
BibRef
Pan, X.[Xuran],
Han, X.Y.[Xiao-Yan],
Wang, C.F.[Chao-Fei],
Li, Z.[Zhuo],
Song, S.[Shiji],
Huang, G.[Gao],
Wu, C.[Cheng],
A unified framework for convolution-based graph neural networks,
PR(155), 2024, pp. 110597.
Elsevier DOI
2408
Laplacian optimization, Graph convolution network,
Graph neural networks, Graph Fourier space, Oversmoothing
BibRef
Eliasof, M.[Moshe],
Treister, E.[Eran],
Global-local graph neural networks for node-classification,
PRL(184), 2024, pp. 103-110.
Elsevier DOI
2408
Graph Neural Networks, Global features, Node classification
BibRef
Li, D.L.[Di-Long],
Lu, C.H.[Cheng-Hui],
Chen, Z.[Ziyi],
Guan, J.L.[Jian-Long],
Zhao, J.[Jing],
Du, J.X.[Ji-Xiang],
Graph Neural Networks in Point Clouds: A Survey,
RS(16), No. 14, 2024, pp. 2518.
DOI Link
2408
BibRef
Shi, Y.[Yan],
Cai, J.X.[Jun-Xiong],
Fan, M.Y.[Ming-Yu],
Feng, W.[Wensen],
Zhang, K.[Kai],
Learning to match features with discriminative sparse graph neural
network,
PR(156), 2024, pp. 110784.
Elsevier DOI
2408
Feature matching, Graph neural network, Visual localization, Attention
BibRef
Byun, J.[Junyoung],
Choi, Y.J.[Yu-Jin],
Lee, J.W.[Jae-Wook],
Improving the utility of differentially private clustering through
dynamical processing,
PR(157), 2025, pp. 110890.
Elsevier DOI
2409
Clustering, Differential privacy, Dynamical processing, Morse theory
BibRef
Guo, N.B.[Ning-Bo],
Jiang, M.Y.[Ming-Yong],
Wang, D.C.[De-Cheng],
Jia, Y.T.[Yu-Tong],
Li, K.T.[Kai-Tao],
Zhang, Y.A.[Yan-An],
Wang, M.D.[Ming-Dong],
Luo, J.C.[Jian-Cheng],
PGNN-Net: Parallel Graph Neural Networks for Hyperspectral Image
Classification Using Multiple Spatial-Spectral Features,
RS(16), No. 18, 2024, pp. 3531.
DOI Link
2410
BibRef
Min, X.[Xin],
Li, W.[Wei],
Han, R.Q.[Rui-Qi],
Ji, T.L.[Tian-Long],
Xie, W.D.[Wei-Dong],
Graph neural collaborative filtering with medical content-aware
pre-training for treatment pattern recommendation,
PRL(185), 2024, pp. 210-217.
Elsevier DOI
2410
Graph neural collaborative filtering, Medical content-aware,
Pre-training generative, Transformer encoder
BibRef
Zhang, H.Y.[Hong-Yuan],
Zhu, Y.[Yanan],
Li, X.L.[Xue-Long],
Decouple Graph Neural Networks: Train Multiple Simple GNNs
Simultaneously Instead of One,
PAMI(46), No. 11, November 2024, pp. 7451-7462.
IEEE DOI
2410
Graph neural networks, Training, Boosting, Optimization,
Task analysis, Stochastic processes, Topology, Backward training,
graph neural network
BibRef
Zhao, Y.S.[Yi-Sheng],
Zhu, H.[Huaiyu],
Huan, R.H.[Ruo-Hong],
Bao, Y.Q.[Yao-Qi],
Pan, Y.[Yun],
Heterogeneous Graph Network for Action Detection,
CirSysVideo(34), No. 9, September 2024, pp. 7962-7974.
IEEE DOI Code:
WWW Link.
2410
Cognition, Image edge detection, Task analysis, Transformers,
Semantics, Context modeling, Self-supervised learning, graph network
BibRef
Avery, W.[William],
Munir, M.[Mustafa],
Marculescu, R.[Radu],
Scaling Graph Convolutions for Mobile Vision,
MobileAI24(5857-5865)
IEEE DOI
2410
Instance segmentation, Accuracy, Convolution,
Semantic segmentation, Message passing,
Graph Neural Networks
BibRef
Chen, C.Q.[Chao-Qi],
Wu, Y.[Yushuang],
Dai, Q.Y.[Qi-Yuan],
Zhou, H.Y.[Hong-Yu],
Xu, M.[Mutian],
Yang, S.[Sibei],
Han, X.G.[Xiao-Guang],
Yu, Y.Z.[Yi-Zhou],
A Survey on Graph Neural Networks and Graph Transformers in Computer
Vision: A Task-Oriented Perspective,
PAMI(46), No. 12, December 2024, pp. 10297-10318.
IEEE DOI
2411
Task analysis, Transformers, Point cloud compression,
Visualization, Videos, graph transformers, graph neural networks,
vision and language
BibRef
Jiang, B.[Bo],
Zhang, Z.Y.[Zi-Yan],
Ge, S.[Sheng],
Wang, B.B.[Bei-Bei],
Wang, X.[Xiao],
Tang, J.[Jin],
Learning Graph Attentions via Replicator Dynamics,
PAMI(46), No. 12, December 2024, pp. 7720-7727.
IEEE DOI
2411
Task analysis, Sparse matrices, Minimization,
Computational modeling, Complexity theory,
replicator dynamics
BibRef
Guo, J.W.[Jing-Wei],
Huang, K.[Kaizhu],
Zhang, R.[Rui],
Yi, X.P.[Xin-Ping],
ES-GNN: Generalizing Graph Neural Networks Beyond Homophily With Edge
Splitting,
PAMI(46), No. 12, December 2024, pp. 11345-11360.
IEEE DOI
2411
Measurement, Noise reduction, Graph neural networks, Data mining,
Standards, Social networking (online), Graph neural networks,
graph mining
BibRef
Shi, Z.H.[Zhi-Hao],
Wang, J.[Jie],
Lu, F.[Fanghua],
Chen, H.Z.[Han-Zhu],
Lian, D.[Defu],
Wang, Z.[Zheng],
Ye, J.P.[Jie-Ping],
Wu, F.[Feng],
Label Deconvolution for Node Representation Learning on Large-Scale
Attributed Graphs Against Learning Bias,
PAMI(46), No. 12, December 2024, pp. 11273-11286.
IEEE DOI
2411
Training, Graph neural networks, Feature extraction, Proteins,
Deconvolution, Linear programming, Scalability, Attributed graphs,
pre-trained models
BibRef
Luo, S.J.[Shi-Jie],
Li, H.[He],
Huang, J.B.[Jian-Bin],
Ma, X.[Xiaoke],
Cui, J.T.[Jiang-Tao],
Qiao, S.J.[Shao-Jie],
Yoo, J.[Jaesoo],
Group link prediction in bipartite graphs with graph neural networks,
PR(158), 2025, pp. 110977.
Elsevier DOI
2411
Bipartite graphs, Link prediction, Group link prediction,
Graph machine learning, Graph neural networks
BibRef
Ali, W.[Waqar],
Vascon, S.[Sebastiano],
Stadelmann, T.[Thilo],
Pelillo, M.[Marcello],
Hierarchical Glocal Attention Pooling for Graph Classification,
PRL(186), 2024, pp. 71-77.
Elsevier DOI
2412
Graph Pooling, Cliques Structures, Graph Neural Networks,
Global and Local Graph Structures
BibRef
Sun, C.X.[Chu-Xiong],
Hu, J.[Jie],
Gu, H.M.[Hong-Ming],
Chen, J.P.[Jin-Peng],
Liang, W.[Wei],
Yang, M.C.[Ming-Chuan],
Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced
Training,
PR(160), 2025, pp. 111210.
Elsevier DOI
2501
Graph Neural Networks, Deep learning, Representation learning,
Semi-supervised learning, Label propagation
BibRef
Li, Z.Z.[Zi-Zhuo],
Ma, J.Y.[Jia-Yi],
Learning Feature Matching via Matchable Keypoint-Assisted Graph
Neural Network,
IP(34), 2025, pp. 154-169.
IEEE DOI
2501
Visualization, Graph neural networks, Accuracy, Message passing,
Feature extraction, Artificial neural networks, Transformers,
message passing
BibRef
Li, X.[Xuyan],
Wang, J.[Jie],
Yan, Z.[Zheng],
Can Graph Neural Networks be Adequately Explained? A Survey,
Surveys(57), No. 5, January 2025, pp. xx-yy.
DOI Link
2502
Graph neural networks, explainability, interpretability,
eXplainable artificial intelligence
BibRef
Xu, L.X.[Li-Xiang],
Peng, J.[Jiawang],
Jiang, X.Y.[Xiao-Yi],
Chen, E.H.[En-Hong],
Luo, B.[Bin],
Graph neural network based on graph kernel: A survey,
PR(161), 2025, pp. 111307.
Elsevier DOI Code:
WWW Link.
2502
Graph kernel, Graph neural network, Fusion strategy,
Graph representation learning, Classification ability
BibRef
Cui, S.C.[Shi-Cheng],
Li, D.Q.[De-Qiang],
Zhang, J.[Jing],
Dynamic multi-scale feature augmentation for inductive network
representation learning,
PR(161), 2025, pp. 111250.
Elsevier DOI
2502
GNN-based Inductive Network Representation Learning framework.
Inductive network representation learning,
Graph neural networks, Multi-scale features, Data augmentation
BibRef
Guo, X.L.[Xiang-Lin],
Wang, Y.F.[Yi-Fan],
Liu, H.[Heng],
Xie, H.R.[Hao-Ran],
Cheng, G.[Gary],
Wang, F.L.[Fu Lee],
Steerable Graph Neural Network on Point Clouds via Second-Order
Random Walks,
MultMed(27), 2025, pp. 875-888.
IEEE DOI
2502
Point cloud compression, Feature extraction, Convolution,
Gaussian mixture model, Convolutional neural networks,
steerable graph neural network
BibRef
Gong, C.Y.[Chong-Ya],
Ai, T.H.[Ting-Hua],
Chen, S.Y.[Shi-Yu],
Xiao, T.Y.[Tian-Yuan],
Yu, H.F.[Hua-Fei],
A Multi-Scale Hybrid Scene Geometric Similarity Measurement Method
Using Heterogeneous Graph Neural Network,
IJGI(14), No. 2, 2025, pp. 84.
DOI Link
2502
BibRef
Liu, W.[Wei],
Gao, P.[Peng],
Zhang, H.T.[Hao-Tian],
Li, K.[Ke],
Yang, W.Y.[Wei-Yong],
Wei, X.[Xingshen],
Shu, J.W.[Ji-Wu],
Trace2Vec: Detecting complex multi-step attacks with explainable
graph neural network,
PR(162), 2025, pp. 111363.
Elsevier DOI
2503
Complex multi-step attack, Anomaly detection,
Dynamic heterogeneous graph, Attack sample construction, Explainability
BibRef
Zhang, J.C.[Jin-Chuan],
Hui, B.[Bei],
Zhu, X.Y.[Xun-Yang],
Tian, L.[Ling],
Hua, F.[Fujun],
Temporal knowledge graph reasoning based on discriminative
neighboring semantic learning,
PR(162), 2025, pp. 111392.
Elsevier DOI
2503
Knowledge graph, Graph neural network, Temporal evolution,
Neighboring semantics, Representation learning
BibRef
Shen, C.[Cong],
Liu, X.[Xiang],
Luo, J.W.[Jia-Wei],
Xia, K.[Kelin],
Torsion Graph Neural Networks,
PAMI(47), No. 4, April 2025, pp. 2946-2956.
IEEE DOI
2503
Graph neural networks, Laplace equations, Analytical models,
Mathematical models, Data models, Computational modeling, message passing
BibRef
Spadaro, G.[Gabriele],
Grangetto, M.[Marco],
Fiandrotti, A.[Attilio],
Tartaglione, E.[Enzo],
Giraldo, J.H.[Jhony H.],
WiGNet: Windowed Vision Graph Neural Network,
WACV25(859-868)
IEEE DOI Code:
WWW Link.
2505
Convolutional codes, Adaptation models, Accuracy,
Computational modeling, Benchmark testing,
Image classification
BibRef
Evani, R.[Ravishankar],
Rajan, D.[Deepu],
Mao, S.[Shangbo],
Multiscale Graph Texture Network,
ECCV24(XXXII: 218-235).
Springer DOI
2412
Code:
WWW Link.
BibRef
Zhang, Q.[Qin],
An, D.S.[Dong-Sheng],
Xiao, T.J.[Tian-Jun],
He, T.[Tong],
Tang, Q.M.[Qing-Ming],
Wu, Y.N.[Ying Nian],
Tighe, J.[Joseph],
Xing, Y.F.[Yi-Fan],
Learning for Transductive Threshold Calibration in Open-World
Recognition,
CVPR24(17097-17106)
IEEE DOI
2410
Measurement, Training, Visualization, Benchmark testing, Robustness,
Graph neural networks, Posthoc calibration, deep metric learning,
open world recognition
BibRef
Dupty, M.H.[Mohammed Haroon],
Dong, Y.F.[Yan-Fei],
Leng, S.[Sicong],
Fu, G.J.[Guo-Ji],
Goh, Y.L.[Yong Liang],
Lu, W.[Wei],
Lee, W.S.[Wee Sun],
Constrained Layout Generation with Factor Graphs,
CVPR24(12851-12860)
IEEE DOI
2410
Knowledge engineering, Accuracy, Layout, Human in the loop,
Graph neural networks, Layout generation, AI guided design
BibRef
Wu, Z.J.[Zi-Jian],
Lu, J.[Jun],
Han, J.[Jing],
Bai, L.[Lianfa],
Zhang, Y.[Yi],
Zhao, Z.[Zhuang],
Song, S.Y.[Si-Yang],
Domain Separation Graph Neural Networks for Saliency Object Ranking,
CVPR24(3964-3974)
IEEE DOI Code:
WWW Link.
2410
Codes, Shape, Image edge detection, Noise, Graph neural networks,
Pattern recognition
BibRef
Kim, C.[Chanho],
Fuxin, L.[Li],
Object Dynamics Modeling with Hierarchical Point Cloud-Based
Representations,
CVPR24(20977-20986)
IEEE DOI
2410
Point cloud compression, Solid modeling, Convolution,
Graph neural networks, Cognition
BibRef
Saha, A.[Avishkar],
Mendez, O.[Oscar],
Russell, C.[Chris],
Bowden, R.[Richard],
Learning Adaptive Neighborhoods for Graph Neural Networks,
ICCV23(22484-22493)
IEEE DOI
2401
BibRef
Aflalo, A.[Amit],
Bagon, S.[Shai],
Kashti, T.[Tamar],
Eldar, Y.[Yonina],
DeepCut: Unsupervised Segmentation using Graph Neural Networks
Clustering,
SG2RL23(32-41)
IEEE DOI
2401
BibRef
Baumann, A.[Anton],
Roßberg, T.[Thomas],
Schmitt, M.[Michael],
Probabilistic MIMO U-Net: Efficient and Accurate Uncertainty
Estimation for Pixel-wise Regression,
Uncertainty23(4500-4508)
IEEE DOI
2401
BibRef
Leventhal, S.[Samuel],
Gyulassy, A.[Attila],
Pascucci, V.[Valerio],
Heimann, M.[Mark],
Modeling Hierarchical Topological Structure in Scientific Images with
Graph Neural Networks,
ICIP23(2995-2999)
IEEE DOI
2312
BibRef
Jing, Y.C.[Yong-Cheng],
Yuan, C.B.[Chong-Bin],
Ju, L.[Li],
Yang, Y.D.[Yi-Ding],
Wang, X.C.[Xin-Chao],
Tao, D.C.[Da-Cheng],
Deep Graph Reprogramming,
CVPR23(24345-24354)
IEEE DOI
2309
BibRef
Fent, F.[Felix],
Bauerschmidt, P.[Philipp],
Lienkamp, M.[Markus],
RadarGNN: Transformation Invariant Graph Neural Network for
Radar-based Perception,
WAD23(182-191)
IEEE DOI
2309
BibRef
Mikula, K.[Karol],
Kollár, M.[Michal],
Ožvat, A.A.[Aneta A.],
Šibíková, M.[Mária],
Cahojová, L.[Lucia],
Natural Numerical Networks on Directed Graphs in Satellite Image
Classification,
SSVM23(339-351).
Springer DOI
2307
BibRef
Bicciato, A.[Alessandro],
Cosmo, L.[Luca],
Minello, G.[Giorgia],
Rossi, L.[Luca],
Torsello, A.[Andrea],
Classifying Me Softly: A Novel Graph Neural Network Based on Features
Soft-Alignment,
SSSPR22(43-53).
Springer DOI
2301
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.Y.[Zi-Yu],
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,
Graph neural networks, 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
Lian, R.[Ruyi],
Ling, H.B.[Hai-Bin],
CheckerPose: Progressive Dense Keypoint Localization for Object Pose
Estimation with Graph Neural Network,
ICCV23(13976-13987)
IEEE DOI Code:
WWW Link.
2401
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 .