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

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

Long Short-Term Memory

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Deep learning

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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

The Expressive Power of Graph Neural Networks: A Survey

Zhang, B., Fan, C., Liu, S., Huang, K., Zhao, X. H., Huang, J., & Liu, Z. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3523700 · Cited by 69

Algorithms going wild – A review of machine learning techniques for terrestrial ecology

Cipriano, C., Noce, S., Mereu, S., & Santini, M. (2025) · Ecological Modelling · doi:10.1016/j.ecolmodel.2025.111164 · Cited by 50

Densely Connected Convolutional Networks With Attention LSTM for Crowd Flows Prediction

Li, W., Tao, W., Qiu, J., Liu, X., Zhou, X., & Pan, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2943890 · Cited by 42

Graph convolutional neural networks with node transition probability-based message passing and DropNode regularization

Huu, T., Nguyen, D. M., Bekoulis, G., Munteanu, A., & Deligiannis, N. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114711 · Cited by 46

Hybrid deep learning model for automating constraint modelling in advanced working packaging

Chengke, W., Wang, X., Wu, P., Wang, J., Jiang, R., Chen, M., & Swapan, M. S. H. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103733 · Cited by 48

GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended AnimationPreprint

Zhang, J., & Meng, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.05729 · Cited by 59

Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power SystemsPreprint

Pagnier, L., & Chertkov, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.06349 · Cited by 45

New Ideas and Trends in Deep Multimodal Content Understanding: A Review

Chen, W., Wang, W., Liu, L., & Lew, M. S. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.10.042 · Cited by 45

A spatial-compositional feature fusion convolutional autoencoder for multivariate geochemical anomaly recognition

Guan, Q., Ren, S., Chen, L., Feng, B., & Yao, Y. (2021) · Computers & Geosciences · doi:10.1016/j.cageo.2021.104890 · Cited by 44

Eigen-GNN: a Graph Structure Preserving Plug-in for GNNs

Zhang, Z., Cui, P., Pei, J., Wang, X., & Zhu, W. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3112746 · Cited by 44

Recommender Systems Based on Graph Embedding Techniques: A Review

Deng, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3174197 · Cited by 50

Graph neural network modeling of grain-scale anisotropic elastic behavior using simulated and measured microscale data

Pagan, D. C., Pash, C. R., Benson, A. R., & Kasemer, M. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00952-y · Cited by 44

TCGNN: Packet-grained network traffic classification via Graph Neural Networks

Hu, G., Xiao, X., Shen, M., Zhang, B., Yan, X., & Liu, Y. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106531 · Cited by 47

Effective PM2.5 concentration forecasting based on multiple spatial–temporal GNN for areas without monitoring stations

Su, I., Chung, Y., Lee, C., & Huang, P.-M. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121074 · Cited by 49

DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules

Du, H., Wang, J., Hui, J., Zhang, L. T., & Wang, H. (2024) · npj Computational Materials · doi:10.1038/s41524-024-01444-x · Cited by 48

MV-GCN: Multi-View Graph Convolutional Networks for Link Prediction

Li, Z., Liu, Z., Huang, J., Tang, G., Duan, Y., Zhang, Z., & Yang, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2957306 · Cited by 44

Neural ranking models for document retrieval

Trabelsi, M., Chen, Z., Davison, B. D., & Heflin, J. (2021) · Information Retrieval · doi:10.1007/s10791-021-09398-0 · Cited by 56

A Novel Method for Hyperspectral Image Classification: Deep Network With Adaptive Graph Structure Integration

Yang, B., Cao, F., & Ye, H. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3150349 · Cited by 44

Graph Element Networks: adaptive, structured computation and memoryPreprint

Alet, F., Jeewajee, A. K., Bauzá, M., Rodríguez, A., Lozano‐Pérez, T., & Kaelbling, L. P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.09019 · Cited by 47

Graph neural networks for single-cell omics data: a review of approaches and applications

Li, S., Hua, H., & Chen, S. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf109 · Cited by 43

A Novel Traffic Flow Forecasting Method Based on RNN-GCN and BRB

Zhu, H., Xie, Y., He, W., Sun, C., Zhu, K., Zhou, G., & Ma, N. (2020) · Journal of Advanced Transportation · doi:10.1155/2020/7586154 · Cited by 46

Transient Stability Assessment Based on Gated Graph Neural Network With Imbalanced Data in Internet of Energy

Zhou, X., Guan, X., Sun, D., Jiang, H., Peng, J., Jin, Y., & Zhang, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3127895 · Cited by 43

PU-GACNet: Graph Attention Convolution Network for Point Cloud Upsampling

Han, B., Zhang, X., & Ren, S. (2022) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104371 · Cited by 43

FedHGCDroid: An Adaptive Multi-Dimensional Federated Learning for Privacy-Preserving Android Malware Classification

Jiang, C., Yin, K., Xia, C., & Huang, W. (2022) · Entropy · doi:10.3390/e24070919 · Cited by 43

PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction

Zhang, S., Zhang, J., Song, X., Adeshina, S., Zheng, D., Faloutsos, C., & Sun, Y. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583511 · Cited by 53

Deep learning criminal networks

Ribeiro, H. V., Lopes, D. G. D., Pessa, A. A. B., Martins, A. F., da Cunha, B. R., Gonçalves, S., Lenzi, E. K., Hanley, Q. S., & Perc, M. (2023) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2023.113579 · Cited by 49

Advances in spatiotemporal graph neural network prediction research

Wang, Y. (2023) · International Journal of Digital Earth · doi:10.1080/17538947.2023.2220610 · Cited by 48

IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research

Khatua, A., Mailthody, V. S., Taleka, B., Ma, T., Song, X., & Hwu, W. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599843 · Cited by 61

DeepScaler: Holistic Autoscaling for Microservices Based on Spatiotemporal GNN with Adaptive Graph Learning

Meng, C., Song, S., Tong, H., Pan, M., & Yu, Y. (2023) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase56229.2023.00038 · Cited by 49

GreedyViG: Dynamic Axial Graph Construction for Efficient Vision GNNs

Munir, M., Avery, W., Rahman, M. M., & Mărculescu, R. (2024) · 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/cvpr52733.2024.00585 · Cited by 47

Artificial intelligence and machine learning applications for cultured meat

Todhunter, M. E., Jubair, S., Verma, R., Saqe, R., Shen, K., & Duffy, B. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1424012 · Cited by 51

Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification

Ma, N., Bu, J., Yang, J., Zhang, Z., Yao, C., Yu, Z., Zhou, S., & Yan, X. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3411951 · Cited by 43

Deep Graph Matching under Quadratic Constraint

Gao, Q., Wang, F., Xue, N., Yu, J.-G., & Xia, G.-S. (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/cvpr46437.2021.00503 · Cited by 43

Recommending Learning Objects Through Attentive Heterogeneous Graph Convolution and Operation-Aware Neural Network

Zhu, Y., Lin, Q., Lü, H., Shi, K., Liu, D., Chambua, J., Wan, S., & Niu, Z. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3125424 · Cited by 43

AugLiChem: data augmentation library of chemical structures for machine learning

Magar, R., Wang, Y., Lorsung, C., Chen, L., Ramasubramanian, H., Li, P., & Farimani, A. B. (2022) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ac9c84 · Cited by 49

Graph neural network-based fault diagnosis: a reviewPreprint

Chen, Z., Xu, J., Alippi, C., Ding, S. X., Shardt, Y. A. W., Peng, T., & Yang, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.08185 · Cited by 82

Abductive Knowledge Induction from Raw Data

Dai, W.-Z., & Muggleton, S. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/254 · Cited by 42

Clipped Hyperbolic Classifiers Are Super-Hyperbolic Classifiers

Guo, Y., Wang, X., Chen, Y., & Yu, S. X. (2022) · 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/cvpr52688.2022.00010 · Cited by 53

Graph neural network based multiple accident diagnosis in nuclear power plants: Data optimization to represent the system configuration

Chae, Y. H., Lee, C., Han, S. M., & Seong, P. H. (2022) · Nuclear Engineering and Technology · doi:10.1016/j.net.2022.02.024 · Cited by 42

Graph Neural Networks Beyond Compromise Between Attribute and Topology

Yang, L., Zhou, W., Peng, W., Niu, B., Gu, J., Wang, C., Cao, X., & He, D. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3512069 · Cited by 42

Ginex

Park, Y., Min, S., & Lee, J. W. (2022) · Proceedings of the VLDB Endowment · doi:10.14778/3551793.3551819 · Cited by 51

Directed message passing neural network (D-MPNN) with graph edge attention (GEA) for property prediction of biofuel-relevant species

Xu, H., Jia, M., Chang, Y., Li, Y., & Wu, S. (2022) · Energy and AI · doi:10.1016/j.egyai.2022.100201 · Cited by 44

A multi-model feature fusion model for lithium-ion battery state of health prediction

Yao, X., Chen, G., Hu, L., & PECHT, M. G. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.106051 · Cited by 40

Recent advances and challenges in experiment-oriented polymer informatics

Hatakeyama‐Sato, K. (2022) · Polymer Journal · doi:10.1038/s41428-022-00734-9 · Cited by 46

Combining Reinforcement Learning Algorithms with Graph Neural Networks to Solve Dynamic Job Shop Scheduling Problems

Yang, Z., Bi, L. H., & Jiao, X. (2023) · Processes · doi:10.3390/pr11051571 · Cited by 42

Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular Traffic Prediction

Wang, X., Yang, K., Wang, Z., Feng, J., Zhu, L. Z., Zhao, J., & Deng, C. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10279355 · Cited by 43

Multi-fidelity graph neural network for flow field data fusion of turbomachinery

Li, J., Li, Y., Liu, T., Zhang, D., & Xie, Y. (2023) · Energy · doi:10.1016/j.energy.2023.129405 · Cited by 44

Higher order heterogeneous graph neural network based on node attribute enhancement

Li, C., Fu, J., Yan, Y., Zhao, Z., & Zeng, Q. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122404 · Cited by 42

Network macroscopic fundamental diagram-informed graph learning for traffic state imputation

Xue, J., Ka, E., Feng, Y., & Ukkusuri, S. V. (2024) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2024.102996 · Cited by 45

Exploring bridge maintenance knowledge graph by leveraging GrapshSAGE and text encoding

Gao, Y., Xiong, G., Li, H., & Richards, J. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105634 · Cited by 43

A novel graph neural network method for Alzheimer’s disease classification

Zhou, Z., Wang, Q., An, X., Chen, S., Sun, Y., Wang, G., & Yan, G. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108869 · Cited by 45

Graph Attention Networks for Neural Social Recommendation

Mu, N., Zha, D., He, Y., & Tang, Z. G. (2019) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2019.00183 · Cited by 42

Learning Social Relations and Spatiotemporal Trajectories for Next Check-in Inference

Liang, W., & Zhang, W. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3016737 · Cited by 40

A Learned Sketch for Subgraph Counting

Zhao, K., Yu, J. X., Zhang, H., Li, Q., & Rong, Y. (2021) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3448016.3457289 · Cited by 45

Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery

Long, Y., Wu, J. Y., Lu, B., Jin, Y., Unberath, M., Liu, Y., Heng, P., & Dou, Q. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561028 · Cited by 42

Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions

Lee, M., Yu, G., Dai, H., & Li, G. Y. (2022) · IEEE Wireless Communications · doi:10.1109/mwc.001.2200023 · Cited by 42

Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial SettingsPreprint

Elinas, P., Bonilla, E. V., & Tiao, L. C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.01852 · Cited by 45

Graph Condensation for Graph Neural NetworksPreprint

Jin, W., Zhao, L., Zhang, S., Liu, Y., Tang, J., & Shah, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.07580 · Cited by 105

Image Co-Saliency Detection and Instance Co-Segmentation Using Attention Graph Clustering Based Graph Convolutional Network

Li, T., Zhang, K., Shen, S., Liu, B., Liu, Q., & Li, Z. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3054526 · Cited by 42

Applications of Neural Networks in Biomedical Data Analysis

Weiss, R., Karimijafarbigloo, S., Roggenbuck, D., & Rödiger, S. (2022) · Biomedicines · doi:10.3390/biomedicines10071469 · Cited by 51

Graph convolutional networks-based method for estimating design loads of complex buildings in the preliminary design stage

Lu, J., Zhang, C., Li, J., Zhao, Y., Qiu, W., Li, T., Zhou, K., & He, J. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119478 · Cited by 41

Graph Neural Network-based Android Malware Classification with Jumping Knowledge

Lo, W. W., Layeghy, S., Sarhan, M., Gallagher, M. R., & Portmann, M. (2022) · IEEE Conference on Dependable and Secure Computing (DSC) · doi:10.1109/dsc54232.2022.9888878 · Cited by 41

Motron: Multimodal Probabilistic Human Motion Forecasting

Salzmann, T., Pavone, M., & Ryll, M. (2022) · 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/cvpr52688.2022.00635 · Cited by 41

Zero-shot surface defect recognition with class knowledge graph

Li, Z., Gao, L., Gao, Y., Li, X., & Li, H. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101813 · Cited by 44

Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price prediction

Costa, D., Cava, L. L., & Tagarelli, A. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583520 · Cited by 44

Few-Shot Relation Extraction With Dual Graph Neural Network Interaction

Li, J., Feng, S., & Chiu, B. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3278938 · Cited by 42

Dynamic Heterogeneous Graph Attention Neural Architecture Search

Zhang, Z., Zhang, Z., Wang, X. Y., Qin, Y., Qin, Z., & Zhu, W. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i9.26338 · Cited by 41

Modern machine‐learning for binding affinity estimation of protein–ligand complexes: Progress, opportunities, and challenges

Harren, T., Gutermuth, T., Grebner, C., Heßler, G., & Rarey, M. (2024) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1716 · Cited by 46

DeepDRA: Drug repurposing using multi-omics data integration with autoencoders

Mohammadzadeh-Vardin, T., Ghareyazi, A., Gharizadeh, A., Abbasi, K., & Rabiee, H. R. (2024) · PLoS ONE · doi:10.1371/journal.pone.0307649 · Cited by 47

Graphic Deep Reinforcement Learning for Dynamic Resource Allocation in Space-Air-Ground Integrated Networks

Cai, Y., Cheng, P., Chen, Z., Xiang, W., Vucetic, B. S., & Li, Y. (2024) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2024.3460086 · Cited by 41

A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications

Zhang, Y., Zhao, Y., Li, Z., Cheng, X., Wang, Y., Kotevska, O., Yu, P. S., & Derr, T. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3454328 · Cited by 57

Graph neural networks for electroencephalogram analysis: Alzheimer’s disease and epilepsy use cases

Abadal, S., Galván, P., Mármol, A., Mammone, N., Ieracitano, C., Giudice, M. L., Salvini, A., & Morabito, F. C. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106792 · Cited by 41

A machine learning approach for wind turbine power forecasting for maintenance planning

Dhungana, H. (2025) · Energy Informatics · doi:10.1186/s42162-024-00459-4 · Cited by 43

Path integral based convolution and pooling for graph neural networks*

Ma, Z., Xuan, J., Wang, Y. G., Li, M., & Lió, P. (2021) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/ac3ae4 · Cited by 43

Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks

Liu, J., Tan, G., Lan, W., & Wang, J. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3437-6 · Cited by 41

KCRec: Knowledge-aware representation Graph Convolutional Network for Recommendation

Zhang, L., Kang, Z., Sun, X., Sun, H. G., Zhang, B., & Pu, D. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107399 · Cited by 42

Sentiment Analysis of Weibo Comments Based on Graph Neural Network

Li, Y., & Li, N. (2022) · IEEE Access · doi:10.1109/access.2022.3154107 · Cited by 40

DAGCRN: Graph convolutional recurrent network for traffic forecasting with dynamic adjacency matrix

Shi, Z., Zhang, Y., Wang, J., Qin, J., Liu, X., Yin, H., & Huang, H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120259 · Cited by 45

Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks

Li, J., Yu, Z., Zhu, Z., Chen, L., Yu, Q., Zheng, Z., Tian, S., Wu, R., & Meng, C. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i7.26034 · Cited by 44

An actor-critic algorithm with policy gradients to solve the job shop scheduling problem using deep double recurrent agents

Monaci, M., Agasucci, V., & Grani, G. (2023) · European Journal of Operational Research · doi:10.1016/j.ejor.2023.07.037 · Cited by 47

Spatiotemporal graph neural network for multivariate multi-step ahead time-series forecasting of sea temperature

Kim, J., Kim, T., Ryu, J.-G., & Kim, J. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106854 · Cited by 43

DyGCN-LSTM: A dynamic GCN-LSTM based encoder-decoder framework for multistep traffic prediction

Kumar, R., Moreira, J. M., & Chandra, J. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04871-3 · Cited by 44

Multi-site solar irradiance forecasting based on adaptive spatiotemporal graph convolutional network

Zang, H., Zhang, Y., Cheng, L., Ding, T., Wei, Z., & Sun, G. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121313 · Cited by 40

Review of Predicting Synergistic Drug Combinations

Pan, Y., Ren, H., Lan, L., Li, Y., & Huang, T. (2023) · Life · doi:10.3390/life13091878 · Cited by 40

Enhancing financial fraud detection with hierarchical graph attention networks: A study on integrating local and extensive structural information

Shi, F., & Zhao, C. (2023) · Finance research letters · doi:10.1016/j.frl.2023.104458 · Cited by 40

A macro–micro spatio-temporal neural network for traffic prediction

Feng, S., Wei, S., Zhang, J., Li, Y., Ke, J., Chen, G., Zheng, Y., & Yang, H. (2023) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2023.104331 · Cited by 40

Kinetics-constrained neural ordinary differential equations: Artificial neural network models tailored for small data to boost kinetic model development

Fedorov, A. V., Perechodjuk, A., & Linke, D. (2023) · Chemical Engineering Journal · doi:10.1016/j.cej.2023.146869 · Cited by 40

Graph Time-series Modeling in Deep Learning: A Survey

Chen, H., & Eldardiry, H. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3638534 · Cited by 43

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules

Bassani, D., Parrott, N. J., Manevski, N., & Zhang, J. D. (2024) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2024.2348157 · Cited by 42

Three-dimensional reconstruction of subsurface stratigraphy using machine learning with neighborhood aggregation

Hu, Y., Wang, Z. Z., Guo, X., Kek, H. Y., Ku, T., Goh, S. H., Leung, C. F., Tan, E., & Zhang, Y. (2024) · Engineering Geology · doi:10.1016/j.enggeo.2024.107588 · Cited by 41

A critical review on system architecture, techniques, trends and challenges in intelligent predictive maintenance

Gupta, S., Kumar, A., & Maiti, J. (2024) · Safety Science · doi:10.1016/j.ssci.2024.106590 · Cited by 48

SRSGCN: A novel multi-sensor fault diagnosis method for hydraulic axial piston pump with limited data

Liang, P., Wang, X., Ai, C., Hou, D., & Liu, S. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110563 · Cited by 40

AEGCN: An Autoencoder-Constrained Graph Convolutional Network

Ma, M., Na, S., & Wang, H. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.12.061 · Cited by 40

PCGCN: Partition-Centric Processing for Accelerating Graph Convolutional Network

Tian, C., Ma, L., Yang, Z. C., & Dai, Y. (2020) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps47924.2020.00100 · Cited by 41

Mobile app recommendation via heterogeneous graph neural network in edge computing

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