Xu, X.Y.[Xin-Yi],
Deng, C.[Cheng],
Xie, Y.C.[Yao-Chen],
Ji, S.W.[Shui-Wang],
Group Contrastive Self-Supervised Learning on Graphs,
PAMI(45), No. 3, March 2023, pp. 3169-3180.
IEEE DOI
2302
Task analysis, Proteins, Aggregates, Mutual information, Generators,
Upper bound, Optimization, Graph neural networks,
self-supervised learning
BibRef
Xia, W.[Wei],
Wang, T.X.[Tian-Xiu],
Gao, Q.X.[Quan-Xue],
Yang, M.[Ming],
Gao, X.B.[Xin-Bo],
Graph Embedding Contrastive Multi-Modal Representation Learning for
Clustering,
IP(32), 2023, pp. 1170-1183.
IEEE DOI
2302
Representation learning, Technological innovation, Deep learning,
Correlation, Clustering methods, Transformers, Telecommunications,
self-supervision
BibRef
Fang, U.[Uno],
Li, J.X.[Jian-Xin],
Lu, X.Q.[Xue-Quan],
Mian, A.[Ajmal],
Gu, Z.Q.[Zhao-Quan],
Robust image clustering via context-aware contrastive graph learning,
PR(138), 2023, pp. 109340.
Elsevier DOI
2303
Supervised clustering, Graph convolution network,
Contrastive graph learning, Graph view generation
BibRef
Luo, Z.F.[Zhen-Fei],
Dong, Y.X.[Yi-Xiang],
Zheng, Q.H.[Qing-Hua],
Liu, H.[Huan],
Luo, M.[Minnan],
Dual-channel graph contrastive learning for self-supervised
graph-level representation learning,
PR(139), 2023, pp. 109448.
Elsevier DOI
2304
Contrastive learning, Graph representation learning,
Graph neural networks, Graph classification
BibRef
Zhang, Z.[Zehua],
Sun, S.L.[Shi-Lin],
Ma, G.X.[Gui-Xiang],
Zhong, C.M.[Cai-Ming],
Line graph contrastive learning for link prediction,
PR(140), 2023, pp. 109537.
Elsevier DOI
2305
Line graph, Contrastive learning, Link prediction,
Node classification, Mutual information
BibRef
Miao, J.X.[Jia-Xing],
Cao, F.L.[Fei-Long],
Li, M.[Ming],
Yang, B.[Bing],
Ye, H.L.[Hai-Liang],
Triplet teaching graph contrastive networks with self-evolving
adaptive augmentation,
PR(142), 2023, pp. 109687.
Elsevier DOI
2307
Contrastive learning, Graph representation learning,
Graph augmentation, Node classification
BibRef
Zhao, Y.X.[Yun-Xiao],
Bai, L.[Liang],
Contrastive clustering with a graph consistency constraint,
PR(146), 2024, pp. 110032.
Elsevier DOI
2311
Contrastive learning, Contrastive clustering,
Graph consistency constraint, Clustering uncertainty
BibRef
Xia, W.[Wei],
Wang, Q.Q.[Qian-Qian],
Gao, Q.X.[Quan-Xue],
Yang, M.[Ming],
Gao, X.B.[Xin-Bo],
Self-Consistent Contrastive Attributed Graph Clustering With
Pseudo-Label Prompt,
MultMed(25), 2023, pp. 6665-6677.
IEEE DOI
2311
BibRef
Bao, P.[Peng],
Li, J.[Jianian],
Yan, R.[Rong],
Liu, Z.Y.[Zhong-Yi],
Dynamic Graph Contrastive Learning via Maximize Temporal Consistency,
PR(148), 2024, pp. 110144.
Elsevier DOI
2402
Contrastive learning, Dynamic graph, Temporal information
BibRef
Bu, W.X.[Wei-Xin],
Cao, X.F.[Xiao-Feng],
Zheng, Y.Z.[Yi-Zhen],
Pan, S.R.[Shi-Rui],
Improving Augmentation Consistency for Graph Contrastive Learning,
PR(148), 2024, pp. 110182.
Elsevier DOI Code:
WWW Link.
2402
Graph contrastive learning, Augmentation consistency
BibRef
Zhou, Q.[Qian],
Wang, Q.Q.[Qian-Qian],
Gao, Q.X.[Quan-Xue],
Yang, M.[Ming],
Gao, X.B.[Xin-Bo],
Unsupervised Discriminative Feature Selection via Contrastive Graph
Learning,
IP(33), 2024, pp. 972-986.
IEEE DOI
2402
Feature extraction, Sparse matrices, Self-supervised learning,
Data models, Telecommunications, Spatial databases, Redundancy,
unsupervised feature selection
BibRef
Gao, Y.[Yuan],
Li, X.[Xin],
Hui, Y.[Yan],
Rethinking Graph Contrastive Learning: An Efficient Single-View
Approach via Instance Discrimination,
MultMed(26), 2024, pp. 3616-3625.
IEEE DOI
2402
Training, Measurement, Costs, Graph neural networks,
Computational modeling, Estimation, Task analysis, uniformity
BibRef
Lee, S.[Soohong],
Lee, S.H.[Sang-Ho],
Lee, J.[Jaehwan],
Lee, W.[Woojin],
Son, Y.[Youngdoo],
Graph contrastive learning with consistency regularization,
PRL(181), 2024, pp. 43-49.
Elsevier DOI
2405
Contrastive learning, Class collision, Consistency regularization,
Graph representation learning, Graph neural network
BibRef
Wang, Y.M.[Yi-Ming],
Chang, D.X.[Dong-Xia],
Fu, Z.Q.[Zhi-Qiang],
Wen, J.[Jie],
Zhao, Y.[Yao],
Partially View-Aligned Representation Learning via Cross-View Graph
Contrastive Network,
CirSysVideo(34), No. 8, August 2024, pp. 7272-7283.
IEEE DOI
2408
Representation learning, Self-supervised learning, Task analysis,
Correlation, Measurement, Visualization, contrastive learning
BibRef
Liu, X.[Xin],
Qian, B.[Biao],
Liu, H.P.[Hai-Peng],
Guo, D.[Dan],
Wang, Y.[Yang],
Wang, M.[Meng],
Seeking False Hard Negatives for Graph Contrastive Learning,
CirSysVideo(34), No. 8, August 2024, pp. 7454-7466.
IEEE DOI
2408
Message passing, Self-supervised learning,
Benchmark testing, Training, Task analysis, message passing
BibRef
Yuan, R.[Ruiwen],
Tang, Y.Q.[Yong-Qiang],
Wu, Y.J.[Ya-Jing],
Niu, J.H.[Jing-Hao],
Zhang, W.[Wensheng],
Semi-Supervised Graph Structure Learning via Dual Reinforcement of
Label and Prior Structure,
Cyber(54), No. 11, November 2024, pp. 6943-6956.
IEEE DOI
2411
Contrastive learning, Task analysis, Correlation, Optimization,
Noise measurement, Topology, Mutual information,
graph structure learning (GSL)
BibRef
Zhang, J.Q.[Jia-Qiang],
Chen, S.C.[Song-Can],
Topology reorganized graph contrastive learning with mitigating
semantic drift,
PR(159), 2025, pp. 111160.
Elsevier DOI
2412
Graph neural network, Self-supervised learning,
Contrastive learning, Node representation learning
BibRef
Xie, Y.[Yu],
Luo, L.[Lianhang],
Cao, T.[Tianpei],
Yu, B.[Bin],
Qin, A.K.,
Contrastive Learning Network for Unsupervised Graph Matching,
CirSysVideo(35), No. 1, January 2025, pp. 643-656.
IEEE DOI
2502
Contrastive learning, Feature extraction, Training,
Noise measurement, Unsupervised learning, Generators,
contrastive learning
BibRef
Chen, X.Y.[Xue-Yuan],
Li, S.Z.[Shang-Zhe],
Liu, R.M.[Ruo-Mei],
Shi, B.[Bowen],
Liu, J.H.[Jia-Heng],
Wu, J.[Junran],
Xu, K.[Ke],
Molecular graph contrastive learning with line graph,
PR(162), 2025, pp. 111380.
Elsevier DOI
2503
Molecular pre-training, Graph contrastive learning,
Dual-helix graph encoder, Hard negative samples, Transfer learning
BibRef
Qin, P.[Peng],
Lu, Y.C.[Yao-Chun],
Chen, W.F.[Wei-Fu],
Li, D.F.[De-Fang],
Feng, G.C.[Guo-Can],
AAGCN: An adaptive data augmentation for graph contrastive learning,
PR(163), 2025, pp. 111471.
Elsevier DOI
2503
Graph augmentation, Graph contrastive learning, Graph semi-supervised learning
BibRef
Li, G.J.[Guo-Jie],
Yu, Z.W.[Zhi-Wen],
Yang, K.X.C.[Kai-Xiang-C],
Lv, J.M.[Jian-Ming],
Chen, C.L.P.[C. L. Philip],
Pseudo-Label Similarity Graph-Driven Multi-View Contrastive
Clustering,
MultMed(28), 2026, pp. 4786-4798.
IEEE DOI
2607
Semantics, Contrastive learning, Optimization, Feature extraction,
Data mining, Autoencoders, Multimedia computing, Limiting, Faces,
contrastive learning
BibRef
Yao, Y.X.[Yu-Xuan],
Peng, B.[Bo],
Qin, T.Y.[Tian-Yi],
Gu, Y.F.[Yan-Feng],
Ling, N.[Nam],
Lei, J.J.[Jian-Jun],
Hypergraph Contrastive Learning for Large-Scale Hyperspectral Image
Clustering,
CirSysVideo(35), No. 7, July 2025, pp. 7090-7100.
IEEE DOI
2507
Contrastive learning, Correlation, Scalability, Robustness,
Learning systems, Hyperspectral imaging, Feature extraction,
clustering information
BibRef
Zou, M.[Minhao],
Wang, Y.T.[Yu-Tong],
Meng, X.F.[Xiao-Feng],
Gan, Z.X.[Zhong-Xue],
Guan, C.[Chun],
Leng, S.Y.[Si-Yang],
Multi-head graph contrastive learning with hop augmentation for node
classification,
PR(170), 2026, pp. 112055.
Elsevier DOI Code:
WWW Link.
2509
Graph contrastive learning, Homophily hypothesis,
Hop augmentation, Node classification
BibRef
Zhou, J.J.[Jun-Jie],
Song, Y.[Yingde],
Chiu, C.[Chinwai],
Xiong, Y.P.[Yong-Ping],
Luo, Y.X.[Yu-Xin],
Song, S.Y.[Si-Yang],
CPG: Contrastive Patch-Graph learning for 3D point cloud,
PR(169), 2026, pp. 111954.
Elsevier DOI
2509
3D point clouds, Graph representation, Patch graph,
Self-supervised learning, Contrastive learning
BibRef
Zhu, X.K.[Xiang-Kai],
Li, C.[Chao],
Yan, Y.[Yeyu],
Fu, J.[Jinhu],
Zhao, Z.Y.[Zhong-Ying],
Zeng, Q.T.[Qing-Tian],
Efficiently Harmonizing Information Sharing for Heterogeneous Graph
Contrastive Learning,
PR(169), 2026, pp. 111873.
Elsevier DOI Code:
WWW Link.
2509
Heterogeneous graph neural network,
Graph representation learning, Graph contrastive learning
BibRef
Zhang, Z.Y.[Zi-Yan],
Jiang, B.[Bo],
Tang, J.[Jin],
Luo, B.[Bin],
Unifying Graph Contrastive Learning via Graph Message Augmentation,
PAMI(47), No. 11, November 2025, pp. 9563-9579.
IEEE DOI
2510
Contrastive learning, Perturbation methods, Data augmentation,
Training, Image edge detection, Computational complexity,
graph data augmentation (GMA)
BibRef
Liu, R.[Ruyue],
Yin, R.[Rong],
Liu, Y.[Yong],
Hao, X.S.[Xiao-Shuai],
Shi, H.C.[Hai-Chao],
Ma, C.[Can],
Wang, W.P.[Wei-Ping],
AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive
Learning,
MultMed(27), 2025, pp. 7810-7820.
IEEE DOI
2510
Contrastive learning, Perturbation methods, Topology,
Laplace equations, Semantics, Noise, Data augmentation,
spectral augmentation
BibRef
Zhang, J.[Jing],
Zhang, W.[Wan],
Jiang, X.Q.[Xiao-Qian],
Xie, Y.J.[Ying-Jie],
Yuan, Y.[Yali],
Meng, S.M.[Shun-Mei],
Zhou, C.Q.[Cang-Qi],
Heterogeneous graph contrastive learning with spectral augmentation
and dual aggregation,
PR(172), 2026, pp. 112505.
Elsevier DOI
2512
Heterogeneous graph neural networks, Contrastive learning,
Spectral augmentation, Dual aggregation
BibRef
Lu, H.[Hu],
Hong, H.T.[Hao-Tian],
Shi, F.[Fuhao],
Wu, S.L.[Sheng-Li],
Duan, L.X.[Li-Xin],
Wan, S.H.[Shao-Hua],
Deep contrastive graph clustering with information preservation,
PR(172), 2026, pp. 112575.
Elsevier DOI Code:
WWW Link.
2601
Graph contrastive learning, Learnable graph data augmentation, Samples selection
BibRef
Zhang, C.H.[Chun-Hui],
Miao, R.[Rui],
Ding, L.Z.[Li-Zhong],
Li, P.Q.[Peng-Qi],
Guo, Y.H.[Yu-Han],
Li, X.[Xingcan],
Yuan, Y.[Ye],
Wang, G.[Guoren],
GCL-GroW: Graph contrastive learning via group whitening,
PR(172), 2026, pp. 112757.
Elsevier DOI Code:
WWW Link.
2601
Graph neural networks, Contrastive learning, Group whitening,
Graph representation learning
BibRef
Ding, Y.[Yuntai],
Ren, T.[Tao],
Wang, Y.F.[Yi-Fan],
Chen, C.[Chong],
Hua, X.S.[Xian-Sheng],
Ju, W.[Wei],
Reversible Column Disentangled Augmentation Tricks for Graph
Contrastive Learning,
MultMed(27), 2025, pp. 9818-9831.
IEEE DOI
2601
Semantics, Contrastive learning, Data augmentation,
Disentangled representation learning, Training,
reversible networks
BibRef
Xu, Y.H.[Yu-Hua],
Wang, J.L.[Jun-Li],
Duan, R.[Rui],
Jiang, C.J.[Chang-Jun],
Layer-Adaptive-Augmentation-Based Graph Contrastive Learning With
Feature Decorrelation,
PAMI(48), No. 2, February 2026, pp. 1750-1761.
IEEE DOI
2601
Contrastive learning, Semantics, Perturbation methods, Noise,
Adaptation models, Robustness, Decorrelation, Data augmentation,
feature decorrelation
BibRef
Wan, S.[Sheng],
Ren, S.[Shougang],
Zhao, Z.C.[Zi-Cheng],
Zhu, Y.[Yan],
Gong, C.[Chen],
NeRS: Negative Relational Smoothing for Graph Contrastive Learning,
PR(179), 2026, pp. 113714.
Elsevier DOI
2606
Graph contrastive learning, Self-supervised learning,
Representation learning, Graph heterophily
BibRef
Singh, K.P.[Kunal Pratap],
Salvador, J.[Jordi],
Weihs, L.[Luca],
Kembhavi, A.[Aniruddha],
Scene Graph Contrastive Learning for Embodied Navigation,
ICCV23(10850-10860)
IEEE DOI
2401
BibRef
Reiß, S.[Simon],
Seibold, C.[Constantin],
Freytag, A.[Alexander],
Rodner, E.[Erik],
Stiefelhagen, R.[Rainer],
Graph-Constrained Contrastive Regularization for Semi-weakly Volumetric
Segmentation,
ECCV22(XXI:401-419).
Springer DOI
2211
BibRef
Tang, S.X.[Shi-Xiang],
Zhu, F.[Feng],
Bai, L.[Lei],
Zhao, R.[Rui],
Wang, C.Y.[Chen-Yu],
Ouyang, W.L.[Wan-Li],
Unifying Visual Contrastive Learning for Object Recognition from a
Graph Perspective,
ECCV22(XXVI:649-667).
Springer DOI
2211
BibRef
Wang, J.[Jin],
Jiang, B.[Bo],
Zero-Shot Learning via Contrastive Learning on Dual Knowledge Graphs,
GSP-CV21(885-892)
IEEE DOI
2112
Knowledge engineering, Learning systems,
Correlation, Semantics, Benchmark testing
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Multiple Kernel Learning, MKL .