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

FRAUDRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance

Zhang, G., Wu, J. X., Yang, J. Y., Beheshti, A., Xue, S., Zhou, C. D., & Sheng, Q. Z. (2021) · Proceedings · doi:10.1109/icdm51629.2021.00098 · Cited by 144

Learning Sparse Latent Graph Representations for Anomaly Detection in Multivariate Time Series

Han, S., & Woo, S. S. (2022) · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3534678.3539117 · Cited by 137

Constructing Neural Network Based Models for Simulating Dynamical Systems

Legaard, C. M., Schranz, T., Schweiger, G., Drgoňa, J., Falay, B., Gomes, C. Â. G., Iosifidis, A., Abkar, M., & Larsen, P. G. (2022) · ACM Computing Surveys · doi:10.1145/3567591 · Cited by 159

Attention-based graph neural networks: a survey

Sun, C., Li, C., Lin, X., Zheng, T., Meng, F., Rui, X., & Wang, Z. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10577-2 · Cited by 156

Joint Type Inference on Entities and Relations via Graph Convolutional Networks

Sun, C., Gong, Y., Wu, Y., Gong, M., Jiang, D., Lan, M., Sun, S., & Duan, N. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1131 · Cited by 139

Learning traffic as a graph: A gated graph wavelet recurrent neural network for network-scale traffic prediction

Cui, Z., Ke, R., Pu, Z., Ma, X., & Wang, Y. (2020) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2020.102620 · Cited by 149

Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey

Liu, X., Yan, M., Deng, L., Li, G., Ye, X., & Fan, D. (2021) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2021.1004311 · Cited by 146

A Review on Graph Neural Network Methods in Financial Applications

Wang, J., Zhang, S., Xiao, Y., & Song, R. (2022) · Journal of Data Science · doi:10.6339/22-jds1047 · Cited by 221

Graph machine learning for integrated multi-omics analysis

Valous, N. A., Popp, F., Zörnig, I., Jäger, D., & Charoentong, P. (2024) · British Journal of Cancer · doi:10.1038/s41416-024-02706-7 · Cited by 152

An Unsupervised Remote Sensing Change Detection Method Based on Multiscale Graph Convolutional Network and Metric Learning

Tang, X., Zhang, H., Mou, L., Liu, F., Zhang, X., Zhu, X. X., & Jiao, L. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3106381 · Cited by 139

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

Botifoll, M., Pinto‐Huguet, I., & Arbiol, J. (2022) · Nanoscale Horizons · doi:10.1039/d2nh00377e · Cited by 147

Hierarchical Graph Pooling with Structure LearningPreprint

Zhang, Z., Bu, J., Ester, M., Zhang, J., Yao, C., Yu, Z. H., & Wang, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.05954 · Cited by 135

Heterogeneous Network Representation Learning Approach for Ethereum Identity Identification

Wang, Y., Liu, Z., Xu, J., & Yan, W. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3164719 · Cited by 136

EGNN: Graph structure learning based on evolutionary computation helps more in graph neural networks

Liu, Z., Yang, D. L., Wang, Y., Lu, M., & Li, R. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110040 · Cited by 134

Graph Neural Network-Based EEG Classification: A Survey

Klepl, D., Wu, M., & He, F. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3355750 · Cited by 152

A Comprehensive Survey on Geometric Deep Learning

Cao, W., Yan, Z., He, Z., & He, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2975067 · Cited by 145

A survey of graph neural networks in various learning paradigms: methods, applications, and challenges

Waikhom, L., & Patgiri, R. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10321-2 · Cited by 228

A comprehensive investigation of multimodal deep learning fusion strategies for breast cancer classification

Nakach, F.-Z., Idri, A., & Göçeri, E. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10984-z · Cited by 146

Deep learning allows genome-scale prediction of Michaelis constants from structural features

Kroll, A., Engqvist, M. K. M., Heckmann, D., & Lercher, M. J. (2021) · PLOS Biology · doi:10.1371/journal.pbio.3001402 · Cited by 132

GraphMix: Improved Training of GNNs for Semi-Supervised Learning

Verma, V., Qu, M., Kawaguchi, K., Lamb, A., Bengio, Y., Kannala, J., & Tang, J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i11.17203 · Cited by 132

Deep reinforcement learning for transportation network combinatorial optimization: A survey

Wang, Q., & Tang, C. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107526 · Cited by 137

Relation Structure-Aware Heterogeneous Graph Neural Network

Zhu, S., Zhou, C. D., Pan, S., Xingquan Zhu, A., & Wang, B. (2019) · Proceedings · doi:10.1109/icdm.2019.00203 · Cited by 130

RouteNet-Fermi: Network Modeling With Graph Neural Networks

Ferriol-Galmés, M., Paillissé, J., Suárez‐Varela, J., Rusek, K., Xiao, S., Shi, X., Cheng, X., Barlet‐Ros, P., & Cabellos‐Aparicio, A. (2023) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2023.3269983 · Cited by 130

DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models

Wen, H., Lin, Y., Xia, Y., Wan, H., Wen, Q., Zimmermann, R., & Liang, Y. (2023) · Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems · doi:10.1145/3589132.3625614 · Cited by 130

SO(3)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials

Heider, Y., Wang, K., & Sun, W. (2020) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2020.112875 · Cited by 131

Are Graph Convolutional Networks With Random Weights Feasible?

Huang, C., Li, M., Cao, F., Fujita, H., Li, Z., & Wu, X. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183143 · Cited by 133

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Khalil, R. A., Safelnasr, Z., Yemane, N., Kedir, M., Shafiqurrahman, A., & Saeed, N. (2024) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2024.3369691 · Cited by 149

FedGNN: Federated Graph Neural Network for Privacy-Preserving RecommendationPreprint

Wu, C., Wu, F., Cao, Y., Huang, Y., & Xie, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.04925 · Cited by 276

Development and use of machine learning algorithms in vaccine target selection

Bravi, B. (2024) · npj Vaccines · doi:10.1038/s41541-023-00795-8 · Cited by 145

ECG: Edge-aware Point Cloud Completion with Graph Convolution

Pan, L. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2994483 · Cited by 127

Learning to Pre-train Graph Neural Networks

Lu, Y., Jiang, X., Fang, Y., & Shi, C. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i5.16552 · Cited by 128

FedGraph: Federated Graph Learning With Intelligent Sampling

Chen, F., Li, P., Miyazaki, T., & Wu, C. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3125565 · Cited by 133

A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals

Zeng, Z., Yao, Y., Liu, Z., & Sun, M. (2022) · Nature Communications · doi:10.1038/s41467-022-28494-3 · Cited by 134

A Brief Survey of Machine Learning and Deep Learning Techniques for E-Commerce Research

Zhang, X. J., Guo, F., Chen, T., Pan, L., Beliakov, G., & Wu, J. (2023) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer18040110 · Cited by 148

A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction

Fan, J., Bai, J., Li, Z., Ortiz‐Bobea, A., & Gomes, C. P. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i11.21444 · Cited by 149

Superposition Graph Neural Network for offshore wind power prediction

Yu, M., Zhang, Z., Li, X., Yu, J. X., Gao, J., Liu, Z., You, B., Zheng, X., & Yu, R. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.06.024 · Cited by 130

Graph convolutional networks for graphs containing missing features

Taguchi, H., Liu, X., & Murata, T. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.11.016 · Cited by 130

Self-supervised Learning on Graphs: Deep Insights and New DirectionPreprint

Jin, W., Derr, T., Liu, H., Wang, Y., Wang, S., Liu, Z., & Tang, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.10141 · Cited by 253

Diffusion Models in De Novo Drug Design

Alakhdar, A. A., Póczos, B., & Washburn, N. R. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01107 · Cited by 134

DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications

Han, D., Wang, Z., Chen, W., Zhong, Y., Wang, S., Zhang, H., Yang, J., Shi, X., & Yin, X. (2021) · Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3460120.3484589 · Cited by 121

Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph

Liu, Y., Yang, S., Xu, Y., Miao, C., Wu, M., & Zhang, J. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3082948 · Cited by 126

On the Explainability of Natural Language Processing Deep Models

Zini, J. E., & Awad, M. (2022) · ACM Computing Surveys · doi:10.1145/3529755 · Cited by 153

Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities

Suárez‐Varela, J., Almasan, P., Ferriol-Galmés, M., Rusek, K., Geyer, F., Cheng, X., Shi, X., Xiao, S., Scarselli, F., Cabellos‐Aparicio, A., et al. (2022) · IEEE Network · doi:10.1109/mnet.123.2100773 · Cited by 121

Accelerating the integration of ChatGPT and other large‐scale AI models into biomedical research and healthcare

Wang, D., Feng, L., Ye, J., Zou, J., & Zheng, Y. (2023) · MedComm – Future Medicine · doi:10.1002/mef2.43 · Cited by 127

Quantifying Privacy Leakage in Graph Embedding

Duddu, V., Boutet, A., & Shejwalkar, V. (2020) · EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous) · doi:10.1145/3448891.3448939 · Cited by 136

N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to MoleculesPreprint

Liu, S., Demirel, M. F., & Liang, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.09206 · Cited by 125

Co-Embedding of Nodes and Edges With Graph Neural Networks

Jiang, X., Zhu, R., Ji, P., & Li, S. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3029762 · Cited by 122

Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation

Cui, S., Yu, B., Liu, T., Zhang, Z., Wang, X., & Shi, J. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.211 · Cited by 118

Fault Diagnosis of Rolling Bearing Based on WHVG and GCN

Li, C., Mo, L., & Yan, R. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3087834 · Cited by 120

Coupling complementary strategy to flexible graph neural network for quick discovery of coformer in diverse co-crystal materials

Jiang, Y., Yang, Z., Guo, J., Li, H., Liu, Y., Guo, Y., Li, M., & Pu, X. (2021) · Nature Communications · doi:10.1038/s41467-021-26226-7 · Cited by 123

A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion

Sun, A. Y., Jiang, P., Yang, Z., Xie, Y., & Chen, X. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-26-5163-2022 · Cited by 130

Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks

Pablo‐García, S., Morandi, S., Vargas–Hernández, R. A., Jorner, K., Ivković, Ž., López, N., & Aspuru‐Guzik, A. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00437-y · Cited by 125

Review on the Applications of Deep Learning in the Analysis of Gastrointestinal Endoscopy Images

Du, W., Rao, N., Liu, D., Jiang, H., Luo, C., Li, Z., Gan, T., & Zeng, B. (2019) · IEEE Access · doi:10.1109/access.2019.2944676 · Cited by 121

Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework

Yang, C. H., Liu, J., & Shi, C. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3450068 · Cited by 120

Learning Video Moment Retrieval Without a Single Annotated Video

Gao, J., & Xu, C. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3075470 · Cited by 118

MVAN: Multi-View Attention Networks for Fake News Detection on Social Media

Ni, S., Li, J., & Kao, H. (2021) · IEEE Access · doi:10.1109/access.2021.3100245 · Cited by 128

Multi-modal graph neural network for early diagnosis of Alzheimer's disease from sMRI and PET scans

Zhang, Y., He, X., Chan, Y. H., Teng, Q., & Rajapakse, J. C. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107328 · Cited by 117

BLoG: Bootstrapped graph representation learning with local and global regularization for recommendation

Li, M., Zhang, L., Cui, L., Bai, L., Li, Z., & Wu, X. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109874 · Cited by 118

GLFANet: A global to local feature aggregation network for EEG emotion recognition

Liu, S., Zhao, Y., An, Y., Zhao, J., Wang, S., & Yan, J. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104799 · Cited by 117

Interpretable Machine Learning Approaches for Forecasting and Predicting Air Pollution: A Systematic Review

Houdou, A., Badisy, I. E., Khomsi, K., Abdala, S. A., Abdulla, F. A., Najmi, H., Obtel, M., Belyamani, L., Ibrahimi, A., & Khalis, M. (2023) · Aerosol and Air Quality Research · doi:10.4209/aaqr.230151 · Cited by 143

Survey of Graph Neural Networks and Applications

Liang, F., Qian, C., Yu, W., GRIFFITH, D. W. T., & Golmie, N. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/9261537 · Cited by 136

Machine learning heralding a new development phase in molecular dynamics simulations

Prašnikar, E., Ljubič, M., Perdih, A., & Borišek, J. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10731-4 · Cited by 122

Adaptive Graph Convolutional Network With Attention Graph Clustering for Co-Saliency Detection

Zhang, K., Li, T., Shen, S., Liu, B., Chen, J., & Liu, Q. (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/cvpr42600.2020.00907 · Cited by 115

Efficient Residential Electric Load Forecasting via Transfer Learning and Graph Neural Networks

Wu, D., & Lin, W. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3208211 · Cited by 114

A Combined Model Based on Recurrent Neural Networks and Graph Convolutional Networks for Financial Time Series Forecasting

Lazcano, A., Herrera, P. J., & Monge, M. (2023) · Mathematics · doi:10.3390/math11010224 · Cited by 122

Machine Learning Methods for Weather Forecasting: A Survey

Zhang, H., Liu, Y., Zhang, C., & Li, N. (2025) · Atmosphere · doi:10.3390/atmos16010082 · Cited by 113

Adversarial Learning Based Node-Edge Graph Attention Networks for Autism Spectrum Disorder Identification

Chen, Y., Yan, J., Jiang, M., Zhang, T., Zhao, Z., Zhao, W., Zheng, J., Yao, D., Zhang, R., Kendrick, K. M., et al. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3154755 · Cited by 113

A review on physics-informed machine learning for process-structure-property modeling in additive manufacturing

Faegh, M., Ghungrad, S., Oliveira, J. P., Rao, P., & Haghighi, A. (2024) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2024.11.066 · Cited by 113

Advancing pharmaceutical research: A comprehensive review of cutting-edge tools and technologies

Chen, J., Lin, A., & Luo, P. (2024) · Current Pharmaceutical Analysis · doi:10.1016/j.cpan.2024.11.001 · Cited by 112

Entity-Oriented Multi-Modal Alignment and Fusion Network for Fake News Detection

Li, P., Sun, X., Yu, H., Tian, Y., Yao, F., & Xu, G. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3098988 · Cited by 112

Scalable deeper graph neural networks for high-performance materials property prediction

Omee, S. S., Louis, S.-Y., Fu, N., Wei, L., Dey, S., Dong, R., Li, Q., & Hu, J. (2022) · Patterns · doi:10.1016/j.patter.2022.100491 · Cited by 124

Hybrid Graph Convolutional Networks for Semi-Supervised Classification

(2019) · International Workshop on Computer Science and Engineering (WCSE) · doi:10.18178/wcse.2019.06.016 · Cited by 113

Learning to Model Relationships for Zero-Shot Video Classification

Gao, J., Zhang, T., & Xu, C. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2985708 · Cited by 111

Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights

Dave, S., Baghdadi, R., Nowatzki, T., Avancha, S., Shrivastava, A., & Li, B. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3098483 · Cited by 120

Amalgamating Knowledge from Heterogeneous Graph Neural Networks

Jing, Y., Yang, Y., Wang, X., Song, M., & Tao, D. (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/cvpr46437.2021.01545 · Cited by 111

Hardware Acceleration of Graph Neural Networks

Auten, A., Tomei, M., & Kumar, R. (2020) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac18072.2020.9218751 · Cited by 112

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

Mekrache, A., Bradai, A., Moulay, E., & Dawaliby, S. (2021) · Vehicular Communications · doi:10.1016/j.vehcom.2021.100398 · Cited by 121

Machine learning for the advancement of membrane science and technology: A critical review

Ignácz, G., Bader, L., Beke, Á. K., Ghunaim, Y., Shastry, T., Vovusha, H., Carbone, M. R., Ghanem, B., & Székely, G. (2024) · Journal of Membrane Science · doi:10.1016/j.memsci.2024.123256 · Cited by 117

Towards Deeper Graph Neural Networks with Differentiable Group NormalizationPreprint

Zhou, K., Huang, X. X., Li, Y., Zha, D., Chen, R., & Hu, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.06972 · Cited by 121

Accurate Learning of Graph Representations with Graph Multiset PoolingPreprint

Baek, J., Kang, M., & Hwang, S. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.11533 · Cited by 163

ParaGraph: Layout Parasitics and Device Parameter Prediction using Graph Neural Networks

Ren, H. M., Kokai, G. F., Turner, W. J., & Ku, T.-S. (2020) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac18072.2020.9218515 · Cited by 112

Short-Term Forecasting Based on Graph Convolution Networks and Multiresolution Convolution Neural Networks for Wind Power

Song, Y., Tang, D., Yu, J., Yu, Z., & Li, X. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3176821 · Cited by 109

A comprehensive review of community detection in graphs

Li, J., Lai, S., Shuai, Z., Tan, Y., Jia, Y., Yu, M., Song, Z., Peng, X., Xu, Z., Ni, Y., et al. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128169 · Cited by 135

Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects

Obaido, G., Mienye, I. D., Egbelowo, O. F., Emmanuel, I. D., Ogunleye, A., Ogbuokiri, B., Mienye, P., & Aruleba, K. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100576 · Cited by 120

Graph Neural Networks with Learnable Structural and Positional RepresentationsPreprint

Dwivedi, V. P., Luu, A. T., Laurent, T., Bengio, Y., & Bresson, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.07875 · Cited by 272

Exploring graph neural networks for semantic enrichment: Room type classification

Wang, Z., Sacks, R., & Yeung, T. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.104039 · Cited by 123

Drug-drug interactions prediction based on deep learning and knowledge graph: A review

Luo, H., Yin, W., Wang, J., Zhang, G., Liang, W., Luo, J., & Yan, C. (2024) · iScience · doi:10.1016/j.isci.2024.109148 · Cited by 119

Network-based multi-omics integrative analysis methods in drug discovery: a systematic review

Jiang, W., Ye, W., Tan, X., & Bao, Y. (2025) · BioData Mining · doi:10.1186/s13040-025-00442-z · Cited by 134

A Note on Over-Smoothing for Graph Neural NetworksPreprint

Cai, C., & Wang, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.13318 · Cited by 312

Music Recommendation via Hypergraph Embedding

Gatta, V. L., Moscato, V., Pennone, M., Postiglione, M., & Sperlí, G. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3146968 · Cited by 106

Semi-supervised multi-scale attention-aware graph convolution network for intelligent fault diagnosis of machine under extremely-limited labeled samples

Xie, Z., Chen, J., Feng, Y., & He, S. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.08.007 · Cited by 109

GraphNorm: A Principled Approach to Accelerating Graph Neural Network TrainingPreprint

Cai, T., Luo, S., Xu, K., He, D., Liu, T., & Wang, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.03294 · Cited by 123

Modular design patterns for hybrid learning and reasoning systems

van Bekkum, M. A., de Boer, M., van Harmelen, F., Meyer-Vitali, A., & ten Teije, A. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02394-3 · Cited by 137

STGNN-TTE: Travel time estimation via spatial–temporal graph neural network

Jin, G., Wang, M., Zhang, J., Sha, H., & Huang, J. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.07.012 · Cited by 104

The topology of fMRI-based networks defines the performance of a graph neural network for the classification of patients with major depressive disorder

Николаевна, П. Е., Maximenko, V. A., Kurkin, S., Sergeev, A., Stoyanov, D., Paunova, R., Kandilarova, S., Simeonova, D., & Hramov, A. E. (2023) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2022.113041 · Cited by 105

ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement Learning

Chen, T., Yan, S., Guo, J., & Wu, W. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3272331 · Cited by 105

Applicability analysis of transformer to wind speed forecasting by a novel deep learning framework with multiple atmospheric variables

Jiang, W., Liu, B., Liang, Y., Gao, H., Lin, P., Zhang, D., & Hu, G. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.122155 · Cited by 108

Automated detection and forecasting of COVID-19 using deep learning techniques: A review

Shoeibi, A., Khodatars, M., Jafari, M., Ghassemi, N., Sadeghi, D., Moridian, P., Khadem, A. A., Alizadehsani, R., Hussain, S., Zare, A., et al. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127317 · Cited by 306

GNN-RE: Graph Neural Networks for Reverse Engineering of Gate-Level Netlists

Alrahis, L., Sengupta, A., Knechtel, J., Patnaik, S., Saleh, H. H., Mohammad, B. S., Al‐Qutayri, M., & Sinanoglu, O. (2021) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2021.3110807 · Cited by 103

Leveraging Power Grid Topology in Machine Learning Assisted Optimal Power Flow

Falconer, T., & Mones, L. (2022) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2022.3187218 · Cited by 114

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