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

Neighbor enhanced graph convolutional networks for node classification and recommendation

Chen, H., Huang, Z., Xu, Y., Deng, Z., Huang, F., He, P. J., & Li, Z. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108594 · Cited by 60

Deep clustering of small molecules at large-scale via variational autoencoder embedding and K-means

Hadipour, H., Liu, C., Davis, R. L., Cardona, S. T., & Hu, P. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04667-1 · Cited by 63

Physics-informed graph neural network for spatial-temporal production forecasting

Liu, W., & Pyrcz, M. J. (2023) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2023.211486 · Cited by 57

DAGAD: Data Augmentation for Graph Anomaly Detection

Liu, F., Ma, X., Wu, J. X., Yang, J. Y., Xue, S., Beheshti, A., Zhou, C. D., Peng, H., Sheng, Q. Z., & Aggarwal, C. C. (2022) · Proceedings · doi:10.1109/icdm54844.2022.00036 · Cited by 63

A hypergraph-based hybrid graph convolutional network for intracity human activity intensity prediction and geographic relationship interpretation

Wang, Y., & Zhu, D. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102149 · Cited by 59

Discovery of antibiotics that selectively kill metabolically dormant bacteria

Zheng, E. J., Valeri, J. A., Andrews, I. W., Krishnan, A., Bandyopadhyay, P., Anahtar, M. N., Herneisen, A. L., Schulte, F., Linnehan, B., Wong, F. J., et al. (2023) · Cell chemical biology · doi:10.1016/j.chembiol.2023.10.026 · Cited by 58

Fault detection in complex mechatronic systems by a hierarchical graph convolution attention network based on causal paths

Zheng, S., Wang, C., Zio, E., & Liu, J. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109872 · Cited by 58

Dynamic Graph Representation Learning With Neural Networks: A Survey

Yang, L., Chatelain, C., & Adam, S. (2024) · IEEE Access · doi:10.1109/access.2024.3378111 · Cited by 71

AI-powered Fraud Detection in Decentralized Finance: A Project Life Cycle Perspective

Luo, B., Zhang, Z., Wang, Q., Ke, A., Lu, S., & He, B. (2024) · ACM Computing Surveys · doi:10.1145/3705296 · Cited by 80

Recent Progress of Artificial Intelligence Application in Polymer Materials

Long, T., Pang, Q., Deng, Y., Pang, X., Zhang, Y., Yang, R., & Zhou, C. (2025) · Polymers · doi:10.3390/polym17121667 · Cited by 64

G3raphGround: Graph-Based Language Grounding

Bajaj, M., Wang, L., & Sigal, L. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00438 · Cited by 57

Topology-Imbalance Learning for Semi-Supervised Node ClassificationPreprint

Chen, D., Lin, Y., Zhao, G., Ren, X., Li, P., Zhou, J., & Sun, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.04099 · Cited by 57

Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlook

Kurshan, E., Shen, H., & Yu, H. (2020) · International Conference on Transdisciplinary AI (TransAI) · doi:10.1109/transai49837.2020.00029 · Cited by 56

TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Search

Zhu, J., Cui, Y., Liu, Y., Sun, H., Li, X., Pelger, M., Yang, T., Zhang, L., Zhang, R., & Zhao, H. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449842 · Cited by 64

Linguistic Steganalysis With Graph Neural Networks

Wu, H., Yi, B., Ding, F., Feng, G., & Zhang, X. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3062233 · Cited by 58

Multimodal Marketing Intent Analysis for Effective Targeted Advertising

Zhang, L., Shen, J., Zhang, J., Xu, J., Li, Z., Yao, Y., & Yu, L. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3073267 · Cited by 56

Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential Recommendation

Chen, G., Zhang, X., Zhao, Y., Xue, C., & Xiang, J. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/197 · Cited by 71

Extracting planar roof structures from very high resolution images using graph neural networks

Zhao, W., Persello, C., & Stein, A. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.02.022 · Cited by 56

DAGAM: a domain adversarial graph attention model for subject-independent EEG-based emotion recognition

Xu, T., Dang, W., Wang, J., & Zhou, Y. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/acae06 · Cited by 56

Dual-Space Graph-Based Interaction Network for RGB-Thermal Semantic Segmentation in Electric Power Scene

Xu, C. X., Li, Q., Jiang, X., Yu, D., & Zhou, Y. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3216313 · Cited by 56

A Comprehensive Survey on Graph Summarization With Graph Neural Networks

Shabani, N., Wu, J. X., Beheshti, A., Sheng, Q. Z., Foo, J., Haghighi, V., Hanif, A., & Shahabikargar, M. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3350545 · Cited by 66

Learning to simulate and design for structural engineeringPreprint

Chang, K.-H., & Cheng, C.-Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.09103 · Cited by 61

Graph representation learning for single-cell biology

Hetzel, L., Fischer, D. S., Günnemann, S., & Theis, F. J. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.05.008 · Cited by 55

Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks

Kacupaj, E., Plepi, J., Singh, K., Thakkar, H., Lehmann, J., & Maleshkova, M. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.72 · Cited by 58

Hybrid-Order Gated Graph Neural Network for Session-Based Recommendation

Chen, Y., Huang, L., Wang, C., & Lai, J. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3091435 · Cited by 55

GoodFloorplan: Graph Convolutional Network and Reinforcement Learning-Based Floorplanning

Xu, Q., Geng, H., Chen, S., Yuan, B., Zhuo, C., Kang, Y., & Wen, X. (2021) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2021.3131550 · Cited by 61

Prediction of gas concentration evolution with evolutionary attention-based temporal graph convolutional network

Cheng, L., Li, L., Li, S., Ran, S., Zhang, Z., & Zhang, Y. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116944 · Cited by 58

Smart wireless health care system using graph LSTM pollution prediction and dragonfly node localization

Bačanin, N., Šarac, M., Budimirović, N., Zivkovic, M. Z., AlZubi, A. A., & Bashir, A. K. (2022) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2022.100711 · Cited by 55

Deep Reinforcement Learning-Based Routing and Spectrum Assignment of EONs by Exploiting GCN and RNN for Feature Extraction

Xu, L., Huang, Y., Xue, Y., & Hu, X. (2022) · Journal of Lightwave Technology · doi:10.1109/jlt.2022.3175865 · Cited by 57

Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systems

He, H., Yu, X., Zhang, J., Song, S., & Letaief, K. B. (2023) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2023.3321667 · Cited by 55

Building3D: An Urban-Scale Dataset and Benchmarks for Learning Roof Structures from Point Clouds

Wang, R., Huang, S., & Yang, H. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01837 · Cited by 55

Drug–Drug Interaction Relation Extraction Based on Deep Learning: A Review

Dou, M., Tang, J., Tiwari, P., Ding, Y., & Guo, F. (2024) · ACM Computing Surveys · doi:10.1145/3645089 · Cited by 56

Advancing drug discovery with deep attention neural networks

Lavecchia, A. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.104067 · Cited by 57

Explaining the Explainers in Graph Neural Networks: a Comparative Study

Longa, A., Azzolin, S., Santin, G., Cencetti, G., Lió, P., Lepri, B., & Passerini, A. (2024) · ACM Computing Surveys · doi:10.1145/3696444 · Cited by 83

Graph Foundation Models: Concepts, Opportunities and Challenges

Liu, J., Yang, C. H., Lu, Z., Chen, J., Li, Y., Zhang, M., Bai, T., Fang, Y., Sun, L., Yu, P. S., et al. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3548729 · Cited by 217

Kolmogorov–Arnold PointNet: Deep learning for prediction of fluid fields on irregular geometries

Kashefi, A. (2025) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2025.117888 · Cited by 55

Explainable artificial intelligence (XAI) in medical imaging: a systematic review of techniques, applications, and challenges

Ahmed, F., Naz, N. S., Khan, S., Rehman, A. U., Ismael, W. M., & Khan, M. A. (2026) · BMC Medical Imaging · doi:10.1186/s12880-025-02118-w · Cited by 79

Predicting Temporal Sets with Deep Neural Networks

Yu, L., Sun, L., Du, B., Liu, C., Xiong, H., & Lv, W. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403152 · Cited by 58

Multilayer network analysis for improved credit risk prediction

Óskarsdóttir, M., & Bravo, C. (2021) · Omega · doi:10.1016/j.omega.2021.102520 · Cited by 55

AutoGraph: Automated Graph Neural Network

Li, Y., & King, I. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-63833-7_16 · Cited by 55

Contrastive Pre-Training of GNNs on Heterogeneous Graphs

Jiang, X., Lu, Y., Fang, Y., & Shi, C. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482332 · Cited by 57

Automating Botnet Detection with Graph Neural NetworksPreprint

Zhou, J., Xu, Z., Rush, A. M., & Yu, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.06344 · Cited by 55

Estimate Three-Phase Distribution Line Parameters With Physics-Informed Graphical Learning Method

Wang, W., & Yu, N. (2021) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2021.3134952 · Cited by 54

Personalized Route Recommendation With Neural Network Enhanced Search Algorithm

Wang, J., Wu, N., & Zhao, W. X. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3068479 · Cited by 54

Multi-View Attention-Guided Multiple Instance Detection Network for Interpretable Breast Cancer Histopathological Image Diagnosis

Li, G., Li, C., Wu, G., Ji, D., & Zhang, H. (2021) · IEEE Access · doi:10.1109/access.2021.3084360 · Cited by 54

Decentralized Inference With Graph Neural Networks in Wireless Communication Systems

Lee, M., Yu, G., & Dai, H. (2021) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3125793 · Cited by 58

Graph correlated attention recurrent neural network for multivariate time series forecasting

Geng, X., He, X., Xu, L., & Yu, J. (2022) · Information Sciences · doi:10.1016/j.ins.2022.04.045 · Cited by 54

Semi-supervised overlapping community detection in attributed graph with graph convolutional autoencoder

He, C., Zheng, Y., Cheng, J., Tang, Y. Z., Chen, G., & Liu, H. (2022) · Information Sciences · doi:10.1016/j.ins.2022.07.036 · Cited by 54

Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging

Qian, J., Li, H., Wang, J., & He, L. (2023) · Diagnostics · doi:10.3390/diagnostics13091571 · Cited by 57

Enhancing aspect-based sentiment analysis using a dual-gated graph convolutional network via contextual affective knowledge

Liu, H., Wu, Y., Li, Q., Lu, W., Li, X., Wei, J., Liu, X., & Feng, J. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126526 · Cited by 55

Financial transaction fraud detector based on imbalance learning and graph neural network

Tong, G., & Shen, J. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110984 · Cited by 54

Heterogeneous graph neural networks for noisy few-shot relation classification

Xie, Y., Xu, H., Jiao-e, L., Yang, C., & Gao, K. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105548 · Cited by 54

Capsule-LPI: a LncRNA–protein interaction predicting tool based on a capsule network

Li, Y., Sun, H., Feng, S., Zhang, Q., Han, S., & Du, W. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04171-y · Cited by 53

Hamiltonian Deep Neural Networks Guaranteeing Nonvanishing Gradients by Design

Galimberti, C. L., Furieri, L., Xu, L. J., & Giancarlo Ferrari-trecate, A. (2023) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2023.3239430 · Cited by 53

The Radically Embodied Conscious Cybernetic Bayesian Brain: From Free Energy to Free Will and Back Again

Safron, A. (2021) · Entropy · doi:10.3390/e23060783 · Cited by 59

DGInet: Dynamic graph and interaction-aware convolutional network for vehicle trajectory prediction

An, J., Liu, W., Liu, Q., Guo, L., Ren, P., & Li, T. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.03.038 · Cited by 53

AFSTGCN: Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network

Xiao, Y., Xia, K., Yin, H., Zhang, Y., Qian, Z., Liu, Z., Liang, Y., & Li, X. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.06.019 · Cited by 53

Early Earthquake Detection Using Batch Normalization Graph Convolutional Neural Network (BNGCNN)

Bilal, M. A., Ji, Y., Wang, Y., Akhter, M. P., & Yaqub, M. (2022) · Applied Sciences · doi:10.3390/app12157548 · Cited by 53

Short-Term Passenger Flow Prediction Using a Bus Network Graph Convolutional Long Short-Term Memory Neural Network Model

Baghbani, A., Bouguila, N., & Patterson, Z. (2022) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981221112673 · Cited by 56

Design Optimization of Truss Structures Using a Graph Neural Network-Based Surrogate Model

Nourian, N., El‐Badry, M., & Jamshidi, M. (2023) · Algorithms · doi:10.3390/a16080380 · Cited by 53

EGNN: Energy-efficient anomaly detection for IoT multivariate time series data using graph neural network

Guo, H., Zhou, Z., Zhao, D., & Gaaloul, W. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.09.028 · Cited by 56

Leveraging machine learning models for peptide–protein interaction prediction

Song, Y., Mi, X., & Shukla, D. (2024) · RSC Chemical Biology · doi:10.1039/d3cb00208j · Cited by 58

A review of automated sleep stage based on EEG signals

Zhang, X., Zhang, X., Huang, Q. N., Lv, Y., & Chen, F. (2024) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2024.06.004 · Cited by 57

Brain-GCN-Net: Graph-Convolutional Neural Network for brain tumor identification

Gürsoy, E., & Kaya, Y. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108971 · Cited by 53

A survey of graph neural networks and their industrial applications

Lu, H., Wang, L., Ma, X., Cheng, J., & Zhou, M. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128761 · Cited by 66

Memory-Based Graph NetworksPreprint

Khasahmadi, A. H., Hassani, K., Moradi, P., Lee, L., & Morris, Q. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.09518 · Cited by 58

Representation Learning for Natural Language Processing

Liu, Z., Lin, Y., & Sun, M. (2023) · Springer eBooks · doi:10.1007/978-981-99-1600-9 · Cited by 61

Domain-Adversarial Network Alignment

Hong, H., Li, X., Pan, Y., & Tsang, I. W.-H. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3023589 · Cited by 52

QSAR modeling without descriptors using graph convolutional neural networks: the case of mutagenicity prediction

Hung, C., & Gini, G. C. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10250-2 · Cited by 58

STGMN: A gated multi-graph convolutional network framework for traffic flow prediction

Ni, Q., & Zhang, M. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03224-w · Cited by 53

Graph Neural Networks for Intelligent Modelling in Network Management and Orchestration: A Survey on Communications

Tam, P., Song, I., Kang, S., Ros, S., & Kim, S. (2022) · Electronics · doi:10.3390/electronics11203371 · Cited by 55

DyGCN: Efficient Dynamic Graph Embedding With Graph Convolutional Network

Cui, Z., Li, Z., Wu, S., Zhang, X., Liu, Q., Wang, L., & Ai, M. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3185527 · Cited by 62

Algorithm selection for protein–ligand docking: strategies and analysis on ACE

Chen, T., Shu, X., Zhou, H., Beckford, F. A., & Mısır, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-35132-5 · Cited by 55

SSF-DDI: a deep learning method utilizing drug sequence and substructure features for drug–drug interaction prediction

Jing, Z., Che, C., Jiang, H., Xu, J., Yin, J., & Zhong, Z. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05654-4 · Cited by 54

A lightweight multi-layer perceptron for efficient multivariate time series forecasting

Wang, Z., Ruan, S., Huang, T., Zhou, H., Zhang, S., Wang, Y., Wang, L., Huang, Z., & Liu, Y. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111463 · Cited by 54

Spatiotemporal Fusion Transformer for large-scale traffic forecasting

Wang, Z., Wang, Y., Jia, F., Zhang, F., Klimenko, N., Wang, L., He, Z., Huang, Z., & Liu, Y. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102293 · Cited by 57

Improving academic performance predictions with dual graph neural networks

Huang, Q., & Zeng, Y. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01344-z · Cited by 52

G2ViT: Graph Neural Network-Guided Vision Transformer Enhanced Network for retinal vessel and coronary angiograph segmentation

Xu, H., & Wu, Y. J. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106356 · Cited by 52

Harnessing AI for solar energy: Emergence of transformer models

Hanif, M. F., & Mi, J. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123541 · Cited by 58

CI-GNN: Building a Category-Instance Graph for Zero-Shot Video Classification

Gao, J., & Xu, C. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.2969787 · Cited by 51

FSS-GCN: A graph convolutional networks with fusion of semantic and structure for emotion cause analysis

Hu, G., Lu, G., & Zhao, Y. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106584 · Cited by 54

Rolling Bearing Fault Diagnosis Based on Horizontal Visibility Graph and Graph Neural Networks

Li, C., Mo, L., & Yan, R. (2020) · International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) · doi:10.1109/icsmd50554.2020.9261687 · Cited by 52

Short-term Hourly Streamflow Prediction with Graph Convolutional GRU NetworksPreprint

Sit, M. A., Demiray, B. Z., & Demir, İ. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.07039 · Cited by 88

Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study

Chen, T., Zhou, K., Duan, K., Zheng, W., Wang, P., Hu, X., & Wang, Z. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3174515 · Cited by 67

Toward Reusable Surrogate Models: Graph-Based Transfer Learning on Trusses

Whalen, E., & Mueller, C. (2021) · Journal of Mechanical Design · doi:10.1115/1.4052298 · Cited by 52

Neuromorphic Camera Denoising Using Graph Neural Network-Driven Transformers

Alkendi, Y., Azzam, R., Ayyad, A., Javed, S., Seneviratne, L. D., & Zweiri, Y. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3201830 · Cited by 55

Multistructure Graph Classification Method With Attention-Based Pooling

Xu, Y., Wang, J., Guang, M., Yan, C., & Jiang, C. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3169219 · Cited by 50

Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning

Palmitessa, R., Grum, M., Engsig‐Karup, A. P., & Löwe, R. (2022) · Water Research · doi:10.1016/j.watres.2022.118972 · Cited by 51

T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation

Huo, C., Jin, D., Li, Y., He, D., Yang, Y.-B., & Wu, L. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25553 · Cited by 51

End‐to‐end generation of structural topology for complex architectural layouts with graph neural networks

Zhang, C., Tao, M., Chen, W., & Fan, J. (2023) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13098 · Cited by 55

Generative BigSMILES: an extension for polymer informatics, computer simulations & ML/AI

Schneider, L., Walsh, D. J., Olsen, B. D., & Pablo, J. (2023) · Digital Discovery · doi:10.1039/d3dd00147d · Cited by 56

High cycle fatigue life prediction of titanium alloys based on a novel deep learning approach

Zhu, S., Zhang, Y., Zhu, B., Zhang, J., He, Y., & We, X. (2024) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2024.108206 · Cited by 52

FusionGCN: Multi-focus image fusion using superpixel features generation GCN and pixel-level feature reconstruction CNN

Ouyang, Y., Zhai, H., Hu, H. G., Li, X., & Zeng, Z. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125665 · Cited by 53

Graph attention-based neural collaborative filtering for item-specific recommendation system using knowledge graph

Elahi, E., Anwar, S., Al-kfairy, M., Rodrigues, J. J. P. C., Ngueilbaye, A., Halim, Z., & Waqas, M. M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126133 · Cited by 51

GraphTER: Unsupervised Learning of Graph Transformation Equivariant Representations via Auto-Encoding Node-Wise Transformations

Gao, X., Hu, W., & Qi, G.-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/cvpr42600.2020.00719 · Cited by 51

Lifelong Graph Learning

Wang, C., Qiu, Y., Gao, D., & Scherer, S. (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/cvpr52688.2022.01335 · Cited by 57

An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy StoragePreprint

Zitnick, C. L., Chanussot, L., Das, A., Goyal, S., Heras‐Domingo, J., Ho, C., Hu, W., Lavril, T., Palizhati, A., Rivière, M., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.09435 · Cited by 115

GNN-Based Network Traffic Analysis for the Detection of Sequential Attacks in IoT

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GraphNET: Graph Neural Networks for routing optimization in Software Defined Networks

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ADGCN: An Asynchronous Dilation Graph Convolutional Network for Traffic Flow Prediction

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

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

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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