Graph neural networks: A review of methods and applications

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

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

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805

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

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325

Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380

Automatically annotated motion tracking identifies a distinct social behavioral profile following chronic social defeat stress

Bordes, J., Miranda, L., Reinhardt, M., Narayan, S. V., Hartmann, J., Newman, E. L., Brix, L. M., van Doeselaar, L., Engelhardt, C., Dillmann, L. J., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40040-3 · Cited by 88

A review of graph neural network applications in mechanics-related domains

Zhao, Y., Li, H., Zhou, H., Attar, H. R., Pfaff, T., & Li, N. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10931-y · Cited by 91

Machine learning applications on neuroimaging for diagnosis and prognosis of epilepsy: A review

Yuan, J., Ran, X., Liu, K., Chen, Y., Yao, Y., Wu, H., & Liu, Q. (2021) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2021.109441 · Cited by 85

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Pichi, F., Moya, B., & Hesthaven, J. S. (2024) · Journal of Computational Physics · doi:10.1016/j.jcp.2024.112762 · Cited by 96

Scalable attack on graph data by injecting vicious nodes

Wang, J., Luo, M., Suya, F., Li, J., Yang, Z., & Zheng, Q. (2020) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-020-00696-7 · Cited by 85

Behavioral Malware Detection using Deep Graph Convolutional Neural Networks

de Oliveira, A. S., & Sassi, R. J. (2021) · International Journal of Computer Applications · doi:10.5120/ijca2021921218 · Cited by 82

MA-GCL: Model Augmentation Tricks for Graph Contrastive Learning

Gong, X., Yang, C. H., & Shi, C. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25547 · Cited by 79

DeepScheduler: Enabling Flow-Aware Scheduling in Time-Sensitive Networking

He, X., Zhuge, X., Dang, F., Wang, X., & Yang, Z. (2023) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom53939.2023.10228875 · Cited by 79

Graph neural networks for clinical risk prediction based on electronic health records: A survey

Boll, H. O., Amirahmadi, A., Ghazani, M. M., de Morais, W. O., de Freitas, E. P., Soliman, A., Etminani, F., Byttner, S., & Recamonde‐Mendoza, M. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104616 · Cited by 86

A Unified Lottery Ticket Hypothesis for Graph Neural NetworksPreprint

Chen, T., Sui, Y., Chen, X., Zhang, A., & Wang, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.06790 · Cited by 79

GraphFormers: GNN-nested Transformers for Representation Learning on Textual GraphPreprint

Yang, J., Liu, Z., Xiao, S., Li, C., Lian, D., Agrawal, S., Singh, A., Sun, G., & Xie, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.02605 · Cited by 89

K-Core Based Temporal Graph Convolutional Network for Dynamic Graphs

Liu, J., Xu, C., Yin, C., Wu, W., & Song, Y. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3033829 · Cited by 84

Fast Haar Transforms for Graph Neural Networks

Li, M., Ma, Z., Wang, Y. G., & Zhuang, X. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.04.028 · Cited by 79

Learning Multi-View Camera Relocalization With Graph Neural Networks

Xue, F., Wu, X., Cai, S., & Wang, J. (2020) · 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/cvpr42600.2020.01139 · Cited by 79

Dynamic evolution of multi-graph based collaborative filtering for recommendation systems

Tang, H., Zhao, G., Bu, X., & Qian, X. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107251 · Cited by 79

Position-Enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations

Huang, L., Ma, Y., Liu, Y., Du, B. D., Wang, S., & Li, D. (2022) · ACM Transactions on Information Systems · doi:10.1145/3511700 · Cited by 81

KPGT

Li, H., Zhao, D. Y., & Zeng, J. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539426 · Cited by 93

A large-scale traffic signal control algorithm based on multi-layer graph deep reinforcement learning

Wang, T., Zhu, Z., Zhang, J., Tian, J., & Zhang, W. (2024) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2024.104582 · Cited by 91

Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities

Vittorio, S., Lunghini, F., Morerio, P., Gadioli, D., Orlandini, S., Silva, P. J., Martinovič, J., Pedretti, A., Bonanni, D., Bue, A. D., et al. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.05.024 · Cited by 88

Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and EvaluationsPreprint

Fang, H., Zhang, D., Shu, Y., & Guo, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.01997 · Cited by 78

A New Framework for Smartphone Sensor-Based Human Activity Recognition Using Graph Neural Network

Mondal, R., Mukherjee, D., Singh, P. K., Bhateja, V., & Sarkar, R. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3015726 · Cited by 79

Mutual CRF-GNN for Few-shot Learning

Tang, S., Chen, D., Bai, L., Liu, K., Ge, Y., & Ouyang, W. (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/cvpr46437.2021.00236 · Cited by 77

Spectral Temporal Graph Neural Network for Trajectory Prediction

Cao, D., Li, J., Ma, H., & Tomizuka, M. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561461 · Cited by 85

A deep graph neural network architecture for modelling spatio-temporal dynamics in resting-state functional MRI data

Azevedo, T., Campbell, A., Romero-García, R., Passamonti, L., Bethlehem, R. A. I., Lió, P., & Toschi, N. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102471 · Cited by 80

TFGAN: Traffic forecasting using generative adversarial network with multi-graph convolutional network

Khaled, A., Elsir, A. M. T., & Shen, Y. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108990 · Cited by 79

Interpretable machine learning methods for predictions in systems biology from omics data

Sidak, D., Schwarzerová, J., Weckwerth, W., & Waldherr, S. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.926623 · Cited by 81

Artificial intelligence-powered pharmacovigilance: A review of machine and deep learning in clinical text-based adverse drug event detection for benchmark datasets

Li, Y., Tao, W., Li, Z., Sun, Z., Fang, L., Fenton, S. H., Xu, H., & Tao, C. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104621 · Cited by 77

Fourier could be a data scientist: From graph Fourier transform to signal processing on graphs

Ricaud, B., Borgnat, P., Tremblay, N., Gonçalvès, P., & Vandergheynst, P. (2019) · Comptes Rendus Physique · doi:10.1016/j.crhy.2019.08.003 · Cited by 80

Residual Correlation in Graph Neural Network Regression

Jia, J., & Benson, A. R. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403101 · Cited by 81

AI in drug development: a multidisciplinary perspective

Gallego, V., Naveiro, R., Roca, C., Insua, D. R., & Campillo, N. E. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10266-8 · Cited by 85

Siamese Earthquake Transformer: A Pair‐Input Deep‐Learning Model for Earthquake Detection and Phase Picking on a Seismic Array

Xiao, Z., Wang, J., Liu, C., Li, J., Zhao, L., & Yao, Z. (2021) · Journal of Geophysical Research Solid Earth · doi:10.1029/2020jb021444 · Cited by 81

Graph Neural Networks for State Estimation in Water Distribution Systems: Application of Supervised and Semisupervised Learning

Xing, L., & Perelman, L. (2022) · Journal of Water Resources Planning and Management · doi:10.1061/(asce)wr.1943-5452.0001550 · Cited by 83

Reliable Representations Make A Stronger Defender

Li, K., Liu, Y., Ao, X., Chi, J., Feng, J., Yang, H., & He, Q. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539484 · Cited by 76

Deep-Graph-Based Reinforcement Learning for Joint Cruise Control and Task Offloading for Aerial Edge Internet of Things (EdgeIoT)

Li, K., Ni, W., Yuan, X., Noor, A., & Jamalipour, A. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3182119 · Cited by 81

Spatial-Temporal Position-Aware Graph Convolution Networks for Traffic Flow Forecasting

Zhao, Y., Lin, Y., Wen, H., Wei, T., Jin, X., & Wan, H. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3220089 · Cited by 82

MobileViG: Graph-Based Sparse Attention for Mobile Vision Applications

Munir, M., Avery, W., & Mărculescu, R. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00215 · Cited by 84

Machine learning prediction of structural dynamic responses using graph neural networks

Li, Q., Wang, Z., Li, L., Hao, H., Chen, W., & Shao, Y. (2023) · Computers & Structures · doi:10.1016/j.compstruc.2023.107188 · Cited by 80

Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks

Zhang, Z., Tian, W., Lu, C., Liao, Z., & Yuan, Z. (2024) · Water Research · doi:10.1016/j.watres.2024.122142 · Cited by 80

Advancing battery safety: Integrating multiphysics and machine learning for thermal runaway prediction in lithium-ion battery module

Goswami, B. R. D., AbdiSobbouhi, Y., Du, H., Mashayek, F., Kingston, T. A., & Yurkiv, V. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.235015 · Cited by 76

Graph neural networks for multivariate time series regression with application to seismic data

Bloemheuvel, S., van den Hoogen, J., Jozinović, D., Michelini, A., & Atzmueller, M. (2023) · International Journal of Data Science and Analytics · doi:10.1007/s41060-022-00349-6 · Cited by 75

Universal Graph Transformer Self-Attention Networks

Nguyen, D. Q., Nguyen, T. D., & Phung, D. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3487553.3524258 · Cited by 92

Graph Deep Learning: State of the Art and Challenges

Georgousis, S., Kenning, M. P., & Xie, X. (2021) · IEEE Access · doi:10.1109/access.2021.3055280 · Cited by 80

Item Relationship Graph Neural Networks for E-Commerce

Liu, W., Zhang, Y., Wang, J., He, Y., Caverlee, J., Chan, P. P. K., Yeung, D., & Heng, P. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3060872 · Cited by 79

Electricity Theft Detection Using Euclidean and Graph Convolutional Neural Networks

Liao, W., Yang, Z., Liu, K., Zhang, B., Chen, X., & Song, R. (2022) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2022.3196403 · Cited by 80

Deep learning, graph-based text representation and classification: a survey, perspectives and challenges

Pham, P., Nguyen, L. T. T., Witold Pedrycz, C., & Vo, B. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10265-7 · Cited by 80

Lithology identification using graph neural network in continental shale oil reservoirs: A case study in Mahu Sag, Junggar Basin, Western China

Lu, G., Zeng, L., Dong, S., Huang, L., Liu, G., Ostadhassan, M., He, W., Du, X., & Bao, C. (2023) · Marine and Petroleum Geology · doi:10.1016/j.marpetgeo.2023.106168 · Cited by 75

Machine learning aided nanoindentation: A review of the current state and future perspectives

Puchi-Cabrera, E. S., Rossi, E., Sansonetti, G., Sebastiani, M., & Bemporad, E. (2023) · Current Opinion in Solid State and Materials Science · doi:10.1016/j.cossms.2023.101091 · Cited by 75

Applied Artificial Intelligence in Materials Science and Material Design

Chávez‐Ángel, E., Eriksen, M., Castro‐Álvarez, A., García, J. H., Botifoll, M., Ávalos‐Ovando, Ó., Arbiol, J., & Mugarza, A. (2025) · Advanced Intelligent Systems · doi:10.1002/aisy.202400986 · Cited by 84

GraphZoom: A multi-level spectral approach for accurate and scalable graph embeddingPreprint

Deng, C., Zhao, Z., Wang, Y., Zhang, Z., & Feng, Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.02370 · Cited by 79

Subgraph Neural NetworksPreprint

Alsentzer, E., Finlayson, S. G., Li, M. M., & Žitnik, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.10538 · Cited by 83

Towards To-a-T Spatio-Temporal Focus for Skeleton-Based Action Recognition

Ke, L., Peng, K., & Lyu, S. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19998 · Cited by 74

Graph convolutional networks in language and vision: A survey

Ren, H., Lu, W., Xiao, Y., Chang, X., Wang, X., Dong, Z., & Fang, D. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109250 · Cited by 77

LGM-GNN: A Local and Global Aware Memory-Based Graph Neural Network for Fraud Detection

Li, P., Yu, H., Luo, X., & Wu, J. X. (2023) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2023.3234529 · Cited by 80

Earthquake Phase Association with Graph Neural Networks

McBrearty, I. W., & Beroza, G. C. (2023) · Bulletin of the Seismological Society of America · doi:10.1785/0120220182 · Cited by 74

Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures

Jirasek, F., & Hasse, H. (2023) · Annual Review of Chemical and Biomolecular Engineering · doi:10.1146/annurev-chembioeng-092220-025342 · Cited by 77

Machine learning applications on air temperature prediction in the urban canopy layer: A critical review of 2011–2022

Wang, H., Yang, J., Chen, G., Ren, C., & Zhang, J. (2023) · Urban Climate · doi:10.1016/j.uclim.2023.101499 · Cited by 81

The Evolution of Distributed Systems for Graph Neural Networks and Their Origin in Graph Processing and Deep Learning: A Survey

Vatter, J., Mayer, R., & Jacobsen, H. (2023) · ACM Computing Surveys · doi:10.1145/3597428 · Cited by 84

Air traffic density prediction using Bayesian ensemble graph attention network (BEGAN)

Xu, Q., Pang, Y., & Liu, Y. (2023) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2023.104225 · Cited by 80

Energy efficient clustering for dense wireless sensor network by applying Graph Neural Networks with coverage metrics

Saadati, M., Mazinani, S. M., Khazaei, A. A., & Chabok, S. J. S. M. (2024) · Ad Hoc Networks · doi:10.1016/j.adhoc.2024.103432 · Cited by 74

A Novel Computational Model for Predicting microRNA–Disease Associations Based on Heterogeneous Graph Convolutional Networks

Li, C., Liu, H., Hu, Q., Que, J., & Yao, J. (2019) · Cells · doi:10.3390/cells8090977 · Cited by 73

A semi-supervised approach to architected materials design using graph neural networks

Guo, K., & Buehler, M. J. (2020) · Extreme Mechanics Letters · doi:10.1016/j.eml.2020.101029 · Cited by 73

Bearing fault diagnosis method based on a multi-head graph attention network

Jiang, L., Li, X., Wu, L., & Li, Y. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac56f1 · Cited by 73

Brain Connectivity Based Graph Convolutional Networks and Its Application to Infant Age Prediction

Li, Y., Zhang, X., Nie, J., Zhang, G., Fang, R., Xu, X., Wu, Z., Hu, D., Wang, L., Zhang, H., et al. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3171778 · Cited by 71

Physically Informed Machine Learning Prediction of Electronic Density of States

Fung, V., Ganesh, P., & Sumpter, B. G. (2022) · Chemistry of Materials · doi:10.1021/acs.chemmater.1c04252 · Cited by 76

Prediction of drug-drug interaction events using graph neural networks based feature extraction

Al-Rabeah, M. H., & Lakizadeh, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-19999-4 · Cited by 83

A systematic method of remaining useful life estimation based on physics-informed graph neural networks with multisensor data

He, Y., Su, H., Zio, E., Peng, S., Fan, L., Yang, Z., Yang, Z., & Zhang, J. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109333 · Cited by 73

Anomaly Detection in Dynamic Graphs: A Comprehensive Survey

Ekle, O. A., & Eberle, W. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3669906 · Cited by 93

Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, Its Applications, and Future Research Trajectories

Yang, N., Chen, S., Zhang, H., & Berry, R. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3405075 · Cited by 83

Review of several key processes in wind power forecasting: Mathematical formulations, scientific problems, and logical relations

Yang, M., Huang, Y., Xu, C., Liu, C., & Dai, B. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124631 · Cited by 73

Motif-Matching Based Subgraph-Level Attentional Convolutional Network for Graph Classification

Peng, H. M., Li, J., Gong, Q., Ning, Y., Wang, S., & He, L. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5987 · Cited by 74

Reducing Communication in Graph Neural Network Training

Tripathy, A., Yelick, K., & Buluç, A. (2020) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc41405.2020.00074 · Cited by 80

MaterialsAtlas.org: a materials informatics web app platform for materials discovery and survey of state-of-the-art

Hu, J., Stefanov, S., Song, Y., Omee, S. S., Louis, S.-Y., Siriwardane, E. M. D., Zhao, Y., & Wei, L. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00750-6 · Cited by 75

Novel approach for burst detection in water distribution systems based on graph neural networks

Zanfei, A., Menapace, A., Brentan, B., Righetti, M., & Herrera, M. (2022) · Sustainable Cities and Society · doi:10.1016/j.scs.2022.104090 · Cited by 80

HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein–Ligand Binding Affinity Prediction

Kyro, G. W., Brent, R. I., & Batista, V. S. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00251 · Cited by 73

BERT- and TF-IDF-based feature extraction for long-lived bug prediction in FLOSS: A comparative study

Gomes, L. A. F., Torres, R. D. S., & Côrtes, M. L. (2023) · Information and Software Technology · doi:10.1016/j.infsof.2023.107217 · Cited by 85

Equivariant quantum circuits for learning on weighted graphs

Skolik, A., Cattelan, M., Yarkoni, S., Bäck, T., & Dunjko, V. (2023) · npj Quantum Information · doi:10.1038/s41534-023-00710-y · Cited by 80

CNNs on surfaces using rotation-equivariant features

Wiersma, R., Eisemann, E., & Hildebrandt, K. (2020) · ACM Transactions on Graphics · doi:10.1145/3386569.3392437 · Cited by 73

ICS-GNN

Gao, J., Chen, J., Li, Z., & Zhang, J. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3447689.3447704 · Cited by 71

Distilling Holistic Knowledge with Graph Neural Networks

Zhou, S., Wang, Y., Chen, D., Chen, J., Wang, X., Wang, C., & Bu, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01022 · Cited by 72

Learning All Dynamics: Traffic Forecasting via Locality-Aware Spatio-Temporal Joint Transformer

Fang, Y., Zhao, F., Qin, Y., Luo, H., & Wang, C. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3197640 · Cited by 71

Knowledge graph representation learning with simplifying hierarchical feature propagation

Li, Z., Zhang, Q., Zhu, F., Li, D., Zheng, C., & Zhang, Y. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103348 · Cited by 75

From intuition to AI: evolution of small molecule representations in drug discovery

McGibbon, M., Shave, S., Dong, J. N., Gao, Y., Houston, D. R., Xie, J., Yang, Y., Schwaller, P., & Blay, V. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad422 · Cited by 77

DA-GNN: A smart contract vulnerability detection method based on Dual Attention Graph Neural Network

Zhen, Z., Zhao, X., Zhang, J., Wang, Y., & Chen, H. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110238 · Cited by 71

Temporal Reasoning Graph for Activity Recognition

Zhang, J., Shen, F., Xu, X., & Shen, H. T. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2985219 · Cited by 70

Spatio-temporal graph convolutional neural network for remaining useful life estimation of aircraft engines

Wang, M., Li, Y., Zhang, Y., & Jia, L. (2020) · Aerospace Systems · doi:10.1007/s42401-020-00070-x · Cited by 70

LR-GNN: a graph neural network based on link representation for predicting molecular associations

Kang, C., Zhang, H., Liu, Z., Huang, S., & Yin, Y. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab513 · Cited by 74

Cross-Domain Recommendation via Progressive Structural Alignment

Zhao, C., Zhao, H., Li, X., He, M. X., Wang, J., & Jianping Fan, B. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3324912 · Cited by 69

Integrating sentiment analysis with graph neural networks for enhanced stock prediction: A comprehensive survey

Das, N., Sadhukhan, B., Chatterjee, R., & Chakrabarti, S. (2024) · Decision Analytics Journal · doi:10.1016/j.dajour.2024.100417 · Cited by 79

A study on ice resistance prediction based on deep learning data generation method

Sun, Q., Chen, J., Zhou, L., Ding, S., & Han, S. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.117467 · Cited by 70

From Single Metals to High‐Entropy Alloys: How Machine Learning Accelerates the Development of Metal Electrocatalysts

Fan, X., Chen, L., Huang, D., Tian, Y., Zhang, X., Jiao, M., & Zhou, Z. P. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202401887 · Cited by 74

Enhancing Communication Networks in the New Era with Artificial Intelligence: Techniques, Applications, and Future Directions

El-Hajj, M. I. (2025) · Network · doi:10.3390/network5010001 · Cited by 95

Implicit Graph Neural NetworksPreprint

Gu, F., Chang, H., Zhu, W., Sojoudi, S., & Ghaoui, L. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.06211 · Cited by 74

GNN-SubNet: disease subnetwork detection with explainable graph neural networks

Pfeifer, B., Saranti, A., & Holzinger, A. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac478 · Cited by 69

RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation

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