Graph neural networks: A review of methods and applications

Deep Residual Learning for Image Recognition

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Attention Is All You Need

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ImageNet classification with deep convolutional neural networks

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Long Short-Term Memory

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Deep learning

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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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Gradient-based learning applied to document recognition

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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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Densely Connected Convolutional Networks

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ImageNet Large Scale Visual Recognition Challenge

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Deep networks for system identification: A survey

Pillonetto, G., Aravkin, A. Y., Gedon, D., Lennart Ljung, A., Ribeiro, A. H., & Schön, T. B. (2024) · Automatica · doi:10.1016/j.automatica.2024.111907 · Cited by 153

Machine intelligence in metamaterials design: a review

Cerniauskas, G., Sadia, H., & Alam, P. (2024) · Oxford Open Materials Science · doi:10.1093/oxfmat/itae001 · Cited by 117

Multi-Meteorological-Factor-Based Graph Modeling for Photovoltaic Power Forecasting

Cheng, L., Zang, H., Ding, T., Wei, Z., & Sun, G. (2021) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2021.3057521 · Cited by 101

Semisupervised Cross-Scale Graph Prototypical Network for Hyperspectral Image Classification

Xi, B., Li, J., Li, Y., Song, R., Xiao, Y., Du, Q., & Chanussot, J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3158280 · Cited by 102

Auto-Metric Graph Neural Network Based on a Meta-Learning Strategy for the Diagnosis of Alzheimer's Disease

Song, X., Mao, M., & Qian, X. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3053568 · Cited by 105

Anatomy-aided deep learning for medical image segmentation: a review

Liu, L., Wolterink, J. M., Brüne, C., & Veldhuis, R. N. J. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/abfbf4 · Cited by 100

AI/ML algorithms and applications in VLSI design and technology

Amuru, D., Zahra, A., Vudumula, H. V., Cherupally, P. K., Gurram, S. R., Ahmad, A., & Abbas, Z. (2023) · Integration · doi:10.1016/j.vlsi.2023.06.002 · Cited by 111

Deep learning of thermodynamics-aware reduced-order models from data

Hernández, Q., Badías, A., González, D., Chinesta, F., & Cueto, E. (2021) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2021.113763 · Cited by 99

Review of Android Malware Detection Based on Deep Learning

Wang, Z., Liu, Q., & Chi, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3028370 · Cited by 108

A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method

Xu, Y., Zhou, B., Jin, S., Xie, X., Chen, Z., Hu, S. H., & He, N. (2022) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2022.101807 · Cited by 104

Deep learning for steganalysis of diverse data types: A review of methods, taxonomy, challenges and future directions

Kheddar, H., Hemis, M., Himeur, Y., Megías, D., & Amira, A. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127528 · Cited by 123

Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs

Fan, A., Gardent, C., Braud, C., & Bordes, A. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1428 · Cited by 101

Application of Deep Learning on Single-Cell RNA Sequencing Data Analysis: A Review

Brendel, M., Su, C., Bai, Z., Zhang, H., Elemento, O., & Wang, F. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.11.011 · Cited by 106

Robust anomaly detection for multivariate time series through temporal GCNs and attention-based VAE

Shi, Y., Wang, B., Yu, Y., Tang, X., Huang, C., & Dong, J. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110725 · Cited by 102

Geometric deep learning for drug discovery

Liu, M., Li, C., Chen, R., Cao, D., & Zeng, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122498 · Cited by 99

Measuring and Improving the Use of Graph Information in Graph Neural NetworksPreprint

Hou, Y., Zhang, J., Cheng, J. S.-C., Ma, K., Ma, R. T. B., Chen, H., & Yang, M.-C. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2206.13170 · Cited by 101

Learning Scheduling Policies for Multi-Robot Coordination With Graph Attention Networks

Wang, Z., & Gombolay, M. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3002198 · Cited by 97

ConsNet: Learning Consistency Graph for Zero-Shot Human-Object Interaction Detection

Liu, Y., Yuan, J., & Chen, C. W. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413600 · Cited by 100

Unreliable Users Detection in Social Media: Deep Learning Techniques for Automatic Detection

Sansonetti, G., Gasparetti, F., D’Aniello, G., & Micarelli, A. (2020) · IEEE Access · doi:10.1109/access.2020.3040604 · Cited by 105

Deep graph similarity learning: a survey

Ma, G., Ahmed, N. K., Willke, T. L., & Yu, P. S. (2021) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-020-00733-5 · Cited by 106

Scenario Understanding and Motion Prediction for Autonomous Vehicles—Review and Comparison

Karle, P., Geisslinger, M., Betz, J., & Lienkamp, M. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3156011 · Cited by 107

Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms

AlQuraishi, M., & Sorger, P. K. (2021) · Nature Methods · doi:10.1038/s41592-021-01283-4 · Cited by 96

Hierarchical graph-based text classification framework with contextual node embedding and BERT-based dynamic fusion

Onan, A. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101610 · Cited by 103

A Systematic Review of Graph Neural Network in Healthcare-Based Applications: Recent Advances, Trends, and Future Directions

Paul, S. G., Saha, A., Hasan, M. Z., Noori, S. R. H., & Moustafa, A. A. (2024) · IEEE Access · doi:10.1109/access.2024.3354809 · Cited by 112

Distributed Graph Neural Network Training: A Survey

Shao, Y., Li, H., Gu, X., Yin, H., Li, Y., Miao, X., Zhang, W., Cui, B., & Chen, L. (2024) · ACM Computing Surveys · doi:10.1145/3648358 · Cited by 127

From Data to Discovery: Recent Trends of Machine Learning in Metal–Organic Frameworks

Park, J., Kim, H., Kang, Y., Lim, Y., & Kim, J. (2024) · JACS Au · doi:10.1021/jacsau.4c00618 · Cited by 102

Unsupervised learning for community detection in attributed networks based on graph convolutional network

Wang, X., Li, J., Yang, L., & Mi, H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.05.058 · Cited by 91

Deep Learning-Based Recommendation System: Systematic Review and Classification

Caiwen, L., Ishak, I., Ibrahim, H., Zolkepli, M., Sidi, F., & Li, C. (2023) · IEEE Access · doi:10.1109/access.2023.3323353 · Cited by 94

Fourier neural operator for real-time simulation of 3D dynamic urban microclimate

Peng, W., Qin, S., Yang, S., Wang, J., Liu, X., & Wang, L. L. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.111063 · Cited by 103

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectorsPreprint

Ju, X., Farrell, S., Calafiura, P., Murnane, D., Prabhat, Gray, L., Klijnsma, T., Pedro, K., Cerati, G. B., Kowalkowski, J., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11603 · Cited by 94

A Hybrid BERT Model That Incorporates Label Semantics via Adjustive Attention for Multi-Label Text Classification

Cai, L., Song, Y., Liu, T., & Zhang, K. (2020) · IEEE Access · doi:10.1109/access.2020.3017382 · Cited by 96

NPI-GNN: Predicting ncRNA–protein interactions with deep graph neural networks

Shen, Z.-A., Luo, T., Zhou, Y.-K., Yu, H., & Du, P.-F. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab051 · Cited by 93

Graph Neural Network (GNN) in Image and Video Understanding Using Deep Learning for Computer Vision Applications

Pradhyumna, P., Shreya, G., & Mohana (2021) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc51422.2021.9532631 · Cited by 93

A survey of drug-target interaction and affinity prediction methods via graph neural networks

Zhang, Y., Hu, Y., Han, N., Yang, A., Liu, X., & Cai, H. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107136 · Cited by 102

Can Graph Neural Networks Count Substructures?Preprint

Chen, Z., Chen, L., Villar, S., & Bruna, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.04025 · Cited by 114

Principles and challenges of modeling temporal and spatial omics data

Velten, B., & Stegle, O. (2023) · Nature Methods · doi:10.1038/s41592-023-01992-y · Cited by 96

ProcessGPT: Transforming Business Process Management with Generative Artificial Intelligence

Beheshti, A., Yang, J., Sheng, Q. Z., Benatallah, B., Casati, F., Dustdar, S., Nezhad, H. R. M., Zhang, X.-Y., & Xue, S. (2023) · 2023 IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws60048.2023.00099 · Cited by 85

Graph Neural Networks for Molecules

Wang, Y., Li, Z., & Farimani, A. B. (2023) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-3-031-37196-7_2 · Cited by 118

Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning

Su, X., Hu, P., Li, D., Zhao, B.-W., Niu, Z., Herget, T., Yu, P. S., & Hu, L. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01312-5 · Cited by 101

2D materials-based next-generation multidimensional photodetectors

Han, J., Fu, Z., Wei, J., Han, S. H., Deng, W., Hu, F., Wang, Z., Zhou, H., He, Y., Gou, J., et al. (2025) · Light Science & Applications · doi:10.1038/s41377-025-01995-8 · Cited by 92

Traffic Demand Prediction Based on Dynamic Transition Convolutional Neural Network

Du, B., Hu, X., Sun, L., Liu, J., Qiao, Y., & Lv, W. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2966498 · Cited by 94

Multivariate time-series classification with hierarchical variational graph pooling

Duan, Z., Xu, H., Wang, Y., Huang, Y., Ren, A., Xu, Z., Sun, Y., & Wang, W. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.07.032 · Cited by 91

Computational methods for analysing multiscale 3D genome organization

Zhang, Y., Boninsegna, L., Yang, M., Misteli, T., Alber, F., & Ma, J. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00638-1 · Cited by 97

General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance

Triguero, I., Molina, D., Poyatos, J., Del Ser, J., & Herrera, F. (2024) · Information Fusion · doi:10.1016/j.inffus.2023.102135 · Cited by 90

Quantitative evaluation of explainable graph neural networks for molecular property prediction

Rao, J., Zheng, S., Lu, Y., & Yang, Y. (2022) · Patterns · doi:10.1016/j.patter.2022.100628 · Cited by 90

ActiveHNE: Active Heterogeneous Network Embedding

Chen, X., Yu, G., Wang, J., Domeniconi, C., Li, Z., & Zhang, X. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/294 · Cited by 97

Policy-GNN: Aggregation Optimization for Graph Neural Networks

Lai, K.-H., Zha, D., Zhou, K., & Hu, X. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403088 · Cited by 90

Mixed Loss Graph Attention Network for Few-Shot SAR Target Classification

Yang, M., Bai, X., Wang, L., & Zhou, F. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3124336 · Cited by 92

Graph Representation Learning Meets Computer Vision: A Survey

Jiao, L., Chen, J., Liu, F., Yang, S., You, C., Liu, X., Li, L., & Hou, B. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3194869 · Cited by 95

Fruit sizing using AI: A review of methods and challenges

Miranda, J. C., Gené-Mola, J., Zude-Sasse, M., Tsoulias, N., Escolà, A., Arnó, J., Rosell-Polo, J. R., Sanz, R., Martínez-Casasnovas, J. A., & Gregorio, E. (2023) · Postharvest Biology and Technology · doi:10.1016/j.postharvbio.2023.112587 · Cited by 99

A Survey on The Expressive Power of Graph Neural NetworksPreprint

Sato, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.04078 · Cited by 200

Graph neural networks for text classification: a survey

Wang, K., Ding, Y., & Han, S. C. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10808-0 · Cited by 108

Opening the Black Box: A systematic review on explainable artificial intelligence in remote sensing

Höhl, A., Obadić, I., Fernández‐Torres, M., Najjar, H., Oliveira, D. A. B., Akata, Z., Dengel, A. R., & Zhu, X. X. (2024) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2024.3467001 · Cited by 116

Multi-Agent Reinforcement Learning for Connected and Automated Vehicles Control: Recent Advancements and Future Prospects

Hua, M., Qi, X., Chen, D., Jiang, K., Liu, Z. E., Sun, H., Zhou, Q., & Xu, H. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3574280 · Cited by 88

Learning Multi-granularity Consecutive User Intent Unit for Session-based Recommendation

Guo, J., Yang, Y., Song, X., Zhang, Y., Wang, Y., Bai, J., & Zhang, Y. (2022) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3488560.3498524 · Cited by 92

Elastic structural analysis based on graph neural network without labeled data

Song, L.-H., Wang, C., Fan, J., & Lu, H. (2022) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12944 · Cited by 90

Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review

Lee, M. (2023) · Molecules · doi:10.3390/molecules28135169 · Cited by 97

Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications

Song, Z., Chen, J., Cheng, J., Chen, G., & Qi, Z. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00223 · Cited by 91

A Survey on Malware Detection with Graph Representation Learning

Bilot, T., Madhoun, N. E., Agha, K. A., & Zouaoui, A. (2024) · ACM Computing Surveys · doi:10.1145/3664649 · Cited by 112

Is Homophily a Necessity for Graph Neural Networks?Preprint

Ma, Y., Liu, X., Shah, N., & Tang, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06134 · Cited by 180

GraphSVX: Shapley Value Explanations for Graph Neural Networks

Duval, A., & Malliaros, F. D. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86520-7_19 · Cited by 91

EdgeNets: Edge Varying Graph Neural Networks

Isufi, E., Gama, F., & Ribeiro, A. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3111054 · Cited by 102

Graph Representation Forecasting of Patient's Medical Conditions: Toward a Digital Twin

Barbiero, P., Viñas, R., & Lió, P. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.652907 · Cited by 92

Multimodal medical image segmentation using multi-scale context-aware network

Wang, X., Li, Z., Huang, Y., & Jiao, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.11.017 · Cited by 87

Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding

Shen, R., Liu, L., Wu, Z., Zhang, Y., Yuan, Z., Guo, J., Yang, F., Zhang, C., Chen, B., Feng, W., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-35288-0 · Cited by 85

Knowledge-guided multi-granularity GCN for ABSA

Zhu, Z., Zhang, D., Li, L., Li, K., Qi, J., Wang, W.-L., Zhang, G., & Liu, P. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103223 · Cited by 88

Capacity estimation of lithium-ion batteries based on data aggregation and feature fusion via graph neural network

Wang, Z., Yang, F., Xu, Q., Wang, Y., Yan, H., & Xie, M. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.120808 · Cited by 87

Graph Neural Networks for Routing Optimization: Challenges and Opportunities

Jiang, W., Han, H., Zhang, Y., Wang, J., He, M., Gu, W., Mu, J., & Cheng, X. (2024) · Sustainability · doi:10.3390/su16219239 · Cited by 116

Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting

Mallick, T., Balaprakash, P., Rask, E., & Macfarlane, J. F. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9413270 · Cited by 86

Artificial intelligence applications in psychoradiology

Li, F. Y., Sun, H., Biswal, B. B., Sweeney, J. A., & Gong, Q. (2021) · Psychoradiology · doi:10.1093/psyrad/kkab009 · Cited by 88

Link Prediction on Complex Networks: An Experimental Survey

Wu, H., Song, C., Ge, Y., & Ge, T. (2022) · Data Science and Engineering · doi:10.1007/s41019-022-00188-2 · Cited by 88

Continuous Graph Neural NetworksPreprint

Xhonneux, L.-P., Qu, M., & Tang, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.00967 · Cited by 86

A Unified Framework for Cross-Domain and Cross-System Recommendations

Zhu, F., Wang, Y., Zhou, J., Chen, C., Li, L., & Liu, G. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3104873 · Cited by 86

CensNet: Convolution with Edge-Node Switching in Graph Neural Networks

Jiang, X., Ji, P., & Li, S. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/369 · Cited by 83

CNN-G: Convolutional Neural Network Combined With Graph for Image Segmentation With Theoretical Analysis

Lu, Y., Chen, Y., Zhao, D., Liu, B., Lai, Z., & Chen, M. (2020) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.2998497 · Cited by 91

Minority-Weighted Graph Neural Network for Imbalanced Node Classification in Social Networks of Internet of People

Wang, K., An, J., Zhou, M., Shi, Z., Shi, X., & Kang, Q. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3200964 · Cited by 90

Material characterization and precise finite element analysis of fiber reinforced thermoplastic composites for 4D printing

Yu, Y., Liu, H., Qian, K., Yang, H., McGehee, M., Gu, J., Luo, D., Yao, L., & Zhang, Y. J. (2020) · Computer-Aided Design · doi:10.1016/j.cad.2020.102817 · Cited by 84

A weighted patient network-based framework for predicting chronic diseases using graph neural networks

Lu, H., & Uddin, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-01964-2 · Cited by 91

MuMiN

Nielsen, D. S., & McConville, R. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531744 · Cited by 114

Artificial intelligence applications in histopathology

Bahadir, C. D., Omar, M., Rosenthal, J., Marchionni, L., Liechty, B., Pisapia, D. J., & Sabuncu, M. R. (2024) · Nature Reviews Electrical Engineering · doi:10.1038/s44287-023-00012-7 · Cited by 93

A Survey on Recommender Systems Using Graph Neural Network

Anand, V., & Maurya, A. K. (2024) · ACM Transactions on Information Systems · doi:10.1145/3694784 · Cited by 94

A Deep Multi-View Framework for Anomaly Detection on Attributed Networks

Peng, Z., Luo, M., Li, J., Xue, L., & Zheng, Q. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3015098 · Cited by 85

Towards Consumer Loan Fraud Detection: Graph Neural Networks with Role-Constrained Conditional Random Field

Xu, B.-B., Shen, H.-W., Sun, B.-J., An, R., Cao, Q., & Cheng, X.-Q. (2021) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i5.16582 · Cited by 106

Theme-Aware Visual Attribute Reasoning for Image Aesthetics Assessment

Li, L., Huang, Y., Wu, J., Yang, Y., Li, Y., Guo, Y., & Shi, G. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3249185 · Cited by 83

Graph neural network-based spatio-temporal indoor environment prediction and optimal control for central air-conditioning systems

Zhang, J., Xiao, F., Li, A., Ma, T., Xu, K., Zhang, H., Yan, R., Fang, X., Li, Y., & Wang, D. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.110600 · Cited by 85

The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review

Keleş, E., & Bağcı, U. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00941-5 · Cited by 99

DB-GPT: Large Language Model Meets Database

Zhou, X., Sun, Z., & Li, G. (2024) · Data Science and Engineering · doi:10.1007/s41019-023-00235-6 · Cited by 83

Kriging Convolutional Networks

Appleby, G., Liu, L., & Liu, L. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5716 · Cited by 88

Hierarchical Multi-View Graph Pooling with Structure Learning

Zhang, Z., Bu, J., Ester, M., Zhang, J., Li, Z., Yao, C., Huifen, D., Yu, Z., & Wang, C. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3090664 · Cited by 287

Representation Learning for Natural Language Processing

Liu, Z., Lin, Y., & Sun, M. (2020) · Springer eBooks · doi:10.1007/978-981-15-5573-2 · Cited by 103

Model Stealing Attacks Against Inductive Graph Neural Networks

Shen, Y. C., He, X., Han, Y., & Zhang, Y. (2022) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46214.2022.9833607 · Cited by 90

Real-time fast charging station recommendation for electric vehicles in coupled power-transportation networks: A graph reinforcement learning method

Xu, P., Zhang, J., Gao, T., Chen, S., Wang, X., Jiang, H., & Gao, W. (2022) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2022.108030 · Cited by 84

Reverse Graph Learning for Graph Neural Network

Peng, L., Hu, R., Kong, F., Gan, J., Mo, Y., Shi, X., & Zhu, X. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3161030 · Cited by 82

Building a Digital Twin for network optimization using Graph Neural Networks

Ferriol-Galmés, M., Suárez‐Varela, J., Paillissé, J., Shi, X., Xiao, S., Cheng, X., Barlet‐Ros, P., & Cabellos‐Aparicio, A. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109329 · Cited by 89

Application of deep learning in ecological resource research: Theories, methods, and challenges

Guo, Q., Jin, S., Li, M., Yang, Q., Xu, K., Ju, Y., Zhang, J., Xuan, J., Liu, J., Su, Y., et al. (2020) · Science China Earth Sciences · doi:10.1007/s11430-019-9584-9 · Cited by 85

Rich Visual Knowledge-Based Augmentation Network for Visual Question Answering

Zhang, L., Liu, S., Liu, D., Zeng, P., Li, X., Song, J., & Gao, L. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3017530 · Cited by 84

Towards Accurate 3D Human Motion Prediction from Incomplete Observations

Cui, Q., & Sun, H. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00477 · Cited by 82

Machine learning for optical fiber communication systems: An introduction and overview

Nevin, J. W., Nallaperuma, S., Shevchenko, N. A., Li, X., Faruk, M. S., & Savory, S. J. (2021) · APL Photonics · doi:10.1063/5.0070838 · Cited by 89

Multi-Scale Cluster-Graph Convolution Network With Multi-Channel Residual Network for Intelligent Fault Diagnosis

Sun, K., Huang, Z., Mao, H., Qin, A., Li, X., Tang, W., & Xiong, J. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3136264 · Cited by 81

Sequence-based drug-target affinity prediction using weighted graph neural networks

Jiang, M., Wang, S., Zhang, S., Zhou, W. G., Zhang, Y., & Li, Z. (2022) · BMC Genomics · doi:10.1186/s12864-022-08648-9 · Cited by 86

Comprehensive Review of Graph Neural Networks: Challenges, Classification, Architectures, Applications, and Potential Utility in Bioinformatics

Malla, A. M., & Banka, A. A. (2025) · Expert Systems · doi:10.1111/exsy.70091 · Cited by 9

A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions

Khemani, B., Patil, S., Kotecha, K. V., & Tanwar, S. (2024) · Journal Of Big Data · doi:10.1186/s40537-023-00876-4 · Cited by 638

Graph Neural Networks: From Foundations to Frontiers Surveying Architectures, Applications, and Future Directions

Hooper, A. (2025) · doi:10.13140/rg.2.2.35362.34244

A Systematic Review of Deep Graph Neural Networks: Challenges, Classification, Architectures, Applications & Potential Utility in Bioinformatics

Malla, A. M., & Banka, A. A. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.02127 · Cited by 7

Graph convolutional networks: a comprehensive review

Zhang, S., Tong, H., Xu, J., & Maciejewski, R. (2019) · Computational Social Networks · doi:10.1186/s40649-019-0069-y · Cited by 1,888