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

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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

Transformer-Based Graph Convolutional Network for Sentiment Analysis

AlBadani, B., Shi, R., Dong, J. L., Al-Sabri, R., & Olouladé, B. M. (2022) · Applied Sciences · doi:10.3390/app12031316 · Cited by 52

Improving Small Molecule pKa Prediction Using Transfer Learning With Graph Neural Networks

Mayr, F., Wieder, M., Wieder, O., & Langer, T. (2022) · Frontiers in Chemistry · doi:10.3389/fchem.2022.866585 · Cited by 50

Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges

Ren, J. F., Xia, F., Lee, I., Hoshyar, A. N., & Aggarwal, C. C. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3570906 · Cited by 55

Tracking down financial statement fraud by analyzing the supplier-customer relationship network

Li, J., Chang, Y., Wang, Y., & Zhu, X. (2023) · Computers & Industrial Engineering · doi:10.1016/j.cie.2023.109118 · Cited by 58

Impact force reconstruction and localization using Distance-assisted Graph Neural Network

Huang, C., Tao, C., Ji, H., & Qiu, J. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110606 · Cited by 50

MG-GCN: Multi-Granularity Graph Convolutional Neural Network for Multi-Label Classification in Multi-Label Information System

Yu, B., Xie, H., Cai, M., & Ding, W. (2023) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2023.3300303 · Cited by 50

Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks

Chen, Z., Chen, F., Zhang, L., Ji, T., Fu, K., Zhao, L., Chen, F., Wu, L., Aggarwal, C. C., & Lu, C. (2023) · ACM Computing Surveys · doi:10.1145/3627816 · Cited by 55

Applications of machine learning and deep learning in SPECT and PET imaging: General overview, challenges and future prospects

Jiménez-Mesa, C., Arco, J. E., Martínez-Murcia, F. J., Suckling, J., Ramı́rez, J., & Górriz, J. M. (2023) · Pharmacological Research · doi:10.1016/j.phrs.2023.106984 · Cited by 62

A Survey of Recent Advances in Deep Learning Models for Detecting Malware in Desktop and Mobile Platforms

Maniriho, P., Mahmood, A. N., & Chowdhury, M. J. M. (2023) · ACM Computing Surveys · doi:10.1145/3638240 · Cited by 70

Medicinal Plant Classification Using Particle Swarm Optimized Cascaded Network

Islam, M. T., Rahman, W., Hossain, M. S., Roksana, K., Azpíroz, I. D., Diaz, R. M., Ashraf, I., & Samad, M. A. (2024) · IEEE Access · doi:10.1109/access.2024.3378262 · Cited by 50

Attention-based dynamic multilayer graph neural networks for loan default prediction

Zandi, S., Korangi, K., Óskarsdóttir, M., Mues, C., & Bravo, C. (2024) · European Journal of Operational Research · doi:10.1016/j.ejor.2024.09.025 · Cited by 57

Richly connected spatial–temporal graph neural network for rotating machinery fault diagnosis with multi-sensor information fusion

Wang, C., Wang, Y., Wang, Y., Li, X., & Chen, Z. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.112230 · Cited by 50

GraLSP: Graph Neural Networks with Local Structural Patterns

Jin, Y., Song, G., & Shi, C. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5861 · Cited by 55

Relational Deep Feature Learning for Heterogeneous Face Recognition

Cho, M., Kim, T., Kim, I.-J., Lee, K., & Lee, S. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.3013186 · Cited by 51

Friend Recommendation Based on Multi-Social Graph Convolutional Network

Chen, L., Xie, Y., Zheng, Z., Zheng, H., & Xie, J. (2020) · IEEE Access · doi:10.1109/access.2020.2977407 · Cited by 51

DeepWeave: Accelerating Job Completion Time with Deep Reinforcement Learning-based Coflow Scheduling

Sun, P., Guo, Z., Wang, J., Li, J., Lan, J., & Hu, Y. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/458 · Cited by 50

DeePaN: deep patient graph convolutional network integrating clinico-genomic evidence to stratify lung cancers for immunotherapy

Fang, C., Xu, D. H., Su, J., Dry, J. R., & Linghu, B. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00381-z · Cited by 53

Charged Particle Tracking via Edge-Classifying Interaction Networks

Dezoort, G., Thais, S. J., Duarte, J. M., Razavimaleki, V., Atkinson, M. J., Ojalvo, I., Neubauer, M. S., & Elmer, P. (2021) · Computing and Software for Big Science · doi:10.1007/s41781-021-00073-z · Cited by 66

Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddingsPreprint

Morris, C., Rattan, G., & Mutzel, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.01543 · Cited by 54

DLGNN: A Double-layer Graph Neural Network Model Incorporating Shopping Sequence Information for Commodity Recommendation

Wei, C., Niu, J., & Guo, Y. (2020) · Sensors and Materials · doi:10.18494/sam.2020.3056 · Cited by 49

Deep Learning in Image Classification: A Survey Report

He, Z. (2020) · International Conference on Information Technology and Computer Application (ITCA) · doi:10.1109/itca52113.2020.00043 · Cited by 56

Understanding Convolutions on Graphs

Daigavane, A., Ravindran, B., & Aggarwal, G. (2021) · Distill · doi:10.23915/distill.00032 · Cited by 63

Graph neural network initialisation of quantum approximate optimisation

Jain, N., Coyle, B., Kashefi, E., & Kumar, N. (2022) · Quantum · doi:10.22331/q-2022-11-17-861 · Cited by 54

Task-Adaptive Few-shot Node Classification

Wang, S., Ding, K., Zhang, C., Chen, C., & Li, J. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539265 · Cited by 52

Networked Knowledge and Complex Networks: An Engineering View

Lü, J., Wen, G., Lu, R., Wang, Y., & Zhang, S. (2022) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2022.105737 · Cited by 52

An Early Warning System for Earthquake Prediction from Seismic Data Using Batch Normalized Graph Convolutional Neural Network with Attention Mechanism (BNGCNNATT)

Bilal, M. A., Ji, Y., Wang, Y., Akhter, M. P., & Yaqub, M. (2022) · Sensors · doi:10.3390/s22176482 · Cited by 49

A machine learning model trained on a high-throughput antibacterial screen increases the hit rate of drug discovery

Rahman, A. S. M. Z., Liu, C., Sturm, H., Hogan, A. M., Davis, R. L., Hu, P., & Cardona, S. T. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010613 · Cited by 55

Causality-based CTR prediction using graph neural networks

Zhai, P., Yang, Y., & Zhang, C. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103137 · Cited by 52

CityTrans: Domain-Adversarial Training With Knowledge Transfer for Spatio-Temporal Prediction Across Cities

Ouyang, X., Yang, Y. Z., Zhou, W., Zhang, Y., Wang, H., & Huang, W. X. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3283520 · Cited by 52

Machine learning for modern power distribution systems: Progress and perspectives

Marković, M., Bossart, M., & Hodge, B. S. (2023) · Journal of Renewable and Sustainable Energy · doi:10.1063/5.0147592 · Cited by 52

Grading of Diabetic Retinopathy Images Based on Graph Neural Network

Feng, M., Wang, J., Wen, K., & Sun, J. (2023) · IEEE Access · doi:10.1109/access.2023.3312709 · Cited by 52

EdgeAISim: A toolkit for simulation and modelling of AI models in edge computing environments

Nandhakumar, A. R., Baranwal, A., Choudhary, P., Golec, M., & Gill, S. S. (2023) · Measurement Sensors · doi:10.1016/j.measen.2023.100939 · Cited by 49

A review on graph neural networks for predicting synergistic drug combinations

Besharatifard, M., & Vafaee, F. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10669-z · Cited by 56

Advancing storm surge forecasting from scarce observation data: A causal-inference based Spatio-Temporal Graph Neural Network approach

Jiang, W., Zhang, J., Li, Y., Zhang, D., Hu, G., Gao, H., & Duan, Z. (2024) · Coastal Engineering · doi:10.1016/j.coastaleng.2024.104512 · Cited by 53

Enhancing academic performance prediction with temporal graph networks for massive open online courses

Huang, Q., & Chen, J. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-00918-5 · Cited by 63

Images and CNN applications in smart agriculture

Sakka, M. E., Mothe, J., & Ivanovici, M. (2024) · European Journal of Remote Sensing · doi:10.1080/22797254.2024.2352386 · Cited by 59

Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors

Odugbemi, A. I., Nyirenda, C., Christoffels, A. G., & Egieyeh, S. A. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.07.003 · Cited by 56

GPTFF: A high-accuracy out-of-the-box universal AI force field for arbitrary inorganic materials

Xie, F., Lu, T., Meng, S., & Liu, M. (2024) · 中国科学通报：英文版 · doi:10.1016/j.scib.2024.08.039 · Cited by 54

Machine learning-guided strategies for reaction conditions design and optimization

Chen, L.-Y., & Li, Y. (2024) · Beilstein Journal of Organic Chemistry · doi:10.3762/bjoc.20.212 · Cited by 50

Deep emotion recognition in textual conversations: a survey

Pereira, P., Moniz, H., & Carvalho, J. P. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-11010-y · Cited by 73

Learning and Reasoning with the Graph Structure Representation in Robotic Surgery

Islam, M., Seenivasan, L., Ming, L. C., & Ren, H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59716-0_60 · Cited by 49

Be Confident! Towards Trustworthy Graph Neural Networks via Confidence CalibrationPreprint

Wang, X., Liu, H., Shi, C., & Yang, C. (2021) · Neural Information Processing Systems · doi:10.48550/arxiv.2109.14285 · Cited by 161

Combinatorial Learning of Graph Edit Distance via Dynamic Embedding

Wang, R., Zhang, T., Yu, T., Yan, J., & Yang, X. (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/cvpr46437.2021.00520 · Cited by 48

A review on deep neural networks for ICD coding

Teng, F., Liu, Y., Li, T., Zhang, Y., Li, S., & Zhao, Y. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3148267 · Cited by 54

Spiking Graph Convolutional Networks

Zhu, Z., Peng, J., Li, J., Chen, L., Yu, Q., & Luo, S. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/338 · Cited by 73

Graph neural networks with constraints of environmental consistency for landslide susceptibility evaluation

Zeng, H., Zhu, Q., Ding, Y., Hu, H., Chen, L., Xie, X. H., Chen, M., & Yao, Y. (2022) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2022.2103819 · Cited by 48

Improving performance and efficiency of Graph Neural Networks by injective aggregation

Dong, W., Wu, J., Zhang, X., Bai, Z., Wang, P., & Woźniak, M. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109616 · Cited by 48

Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks

Yang, Z., & Buehler, M. J. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00879-4 · Cited by 49

Unsupervised anomaly detection using graph neural networks integrated with physical-statistical feature fusion and local-global learning

Feng, C., Liu, C., & Jiang, D. (2023) · Renewable Energy · doi:10.1016/j.renene.2023.02.053 · Cited by 48

Medicare Fraud Detection Using Graph Analysis: A Comparative Study of Machine Learning and Graph Neural Networks

Yoo, Y., Shin, J.-H., & Kyeong, S.-H. (2023) · IEEE Access · doi:10.1109/access.2023.3305962 · Cited by 48

Mesh-based GNN surrogates for time-independent PDEs

Gladstone, R. J., Rahmani, H., Suryakumar, V. S., Meidani, H., D’Elia, M., & Zareei, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-53185-y · Cited by 48

Meta-transfer learning-based method for multi-fault analysis and assessment in power system

Zheng, L., Zhu, Y., & Zhou, Y. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05772-9 · Cited by 48

Graph Neural Networks for building and civil infrastructure operation and maintenance enhancement

Wettewa, S., Hou, L., & Zhang, G. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102868 · Cited by 58

Disease state prediction from single-cell data using graph attention networks

Ravindra, N. G., Sehanobish, A., Pappalardo, J. L., Hafler, D. A., & van Dijk, D. (2020) · ACM Conference on Health, Inference, and Learning (CHIL) · doi:10.1145/3368555.3384449 · Cited by 47

Scalable Graph Neural Network Training

Serafini, M., & Guan, H. (2021) · ACM SIGOPS Operating Systems Review · doi:10.1145/3469379.3469387 · Cited by 55

Act Like a Radiologist: Towards Reliable Multi-View Correspondence Reasoning for Mammogram Mass Detection

Liu, Y., Zhang, F., Chen, C., Wang, S., Wang, Y., & Yu, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3085783 · Cited by 54

Rotation Invariant Graph Neural Networks using Spin ConvolutionsPreprint

Shuaibi, M., Kolluru, A., Das, A., Grover, A., Sriram, A., Ulissi, Z. W., & Zitnick, C. L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09575 · Cited by 63

Review on Learning and Extracting Graph Features for Link Prediction

Mutlu, E. C., Oghaz, T., Rajabi, A., & Garibay, I. (2020) · Machine Learning and Knowledge Extraction · doi:10.3390/make2040036 · Cited by 62

System-Scale-Free Transient Contingency Screening Scheme Based on Steady-State Information: A Pooling-Ensemble Multi-Graph Learning Approach

Huang, J., Guan, L., Su, Y., Yao, H., Guo, M., & Zhong, Z. L. (2021) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2021.3097331 · Cited by 47

Network-based analysis of fluid flows: Progress and outlook

Taira, K., & Nair, A. (2022) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2022.100823 · Cited by 48

Application of machine vision for tool condition monitoring and tool performance optimization–a review

Banda, T., Farid, A. A., Li, C., Jauw, V. L., & Lim, C. S. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-09696-x · Cited by 49

Spatiotemporal analysis of bidimensional wind speed forecasting: Development and thorough assessment of LSTM and ensemble graph neural networks on the Dutch database

Santos, V. O., Rocha, P. A. C., Scott, J., Thé, J. V. G., & Gharabaghi, B. (2023) · Energy · doi:10.1016/j.energy.2023.127852 · Cited by 48

Knowledge‐enhanced graph neural networks for construction material quantity estimation of reinforced concrete buildings

Fei, Y., Liao, W., Lu, X., & Guan, H. (2023) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13094 · Cited by 47

Automated generative design and prefabrication of precast buildings using integrated BIM and graph convolutional neural network

Li, K., Gan, V. J. L., Li, M., Gao, M. Y., Tiong, R. L. K., & Yang, Y. (2024) · Developments in the Built Environment · doi:10.1016/j.dibe.2024.100418 · Cited by 47

Data-Driven Evolutionary Algorithm Based on Inductive Graph Neural Networks for Multimodal Multiobjective Optimization

Dang, Q., Liu, Q., Yang, S., & He, X. (2025) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2025.3541046 · Cited by 49

CAGE: Context-Aware Grasping Engine

Liu, W., Daruna, A., & Chernova, S. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197289 · Cited by 49

GNNLens : A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks

Jin, Z., Wang, Y., Wang, Q., Ming, Y., Ma, T., & Qu, H. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3148107 · Cited by 63

Rubik: A Hierarchical Architecture for Efficient Graph Neural Network Training

Chen, X., Wang, Y., Xie, X., Hu, X., Basak, A., Liang, L., Yan, M., Deng, L., Ding, Y., Du, Z., et al. (2021) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2021.3079142 · Cited by 59

Rapid Approximate Subset-Based Spectra Prediction for Electron Ionization–Mass Spectrometry

Zhu, R. L., & Jonas, E. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.2c02093 · Cited by 46

Coordinated Sum-Rate Maximization in Multicell MU-MIMO With Deep Unrolling

Schynol, L., & Pesavento, M. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3242716 · Cited by 46

A graph neural network (GNN) method for assigning gas calorific values to natural gas pipeline networks

Yang, Z., Liu, Z., Zhou, J., Song, C., Xiang, Q., He, Q., Hu, J., Faber, M. H., Zio, E., Li, Z., et al. (2023) · Energy · doi:10.1016/j.energy.2023.127875 · Cited by 46

Leveraging graph convolutional networks for semi-supervised fault diagnosis of HVAC systems in data-scarce contexts

Fan, C., Lin, Y., Piscitelli, M. S., Chiosa, R., Wang, H., Capozzoli, A., & Ma, Y. (2023) · Building Simulation · doi:10.1007/s12273-023-1041-1 · Cited by 46

Self-Supervised Graph Learning for Long-Tailed Cognitive Diagnosis

Wang, S., Zeng, Z., Yang, X., & Zhang, X. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i1.25082 · Cited by 50

Deep Learning from Phylogenies for Diversification Analyses

Lambert, S., Voznica, J., & Morlon, H. (2023) · Systematic Biology · doi:10.1093/sysbio/syad044 · Cited by 47

A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data

Li, B., Su, S., Zhu, C., Lin, J., Hu, X., Su, L., Yu, Z., Liao, K., & Chen, H. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00732-w · Cited by 52

Improving air quality assessment using physics-inspired deep graph learning

Li, L., Wang, J., Franklin, M., Yin, Q., Wu, J., Camps‐Valls, G., Zhu, Z., Wang, C., Ge, Y., & Reichstein, M. (2023) · npj Climate and Atmospheric Science · doi:10.1038/s41612-023-00475-3 · Cited by 46

Knowledge Graph Enhanced Contextualized Attention-Based Network for Responsible User-Specific Recommendation

Elahi, E., Anwar, S., Shah, B., Halim, Z., Ullah, A., Rida, I., & Waqas, M. M. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3641288 · Cited by 46

Hierarchical FFT-LSTM-GCN based model for nuclear power plant fault diagnosis considering spatio-temporal features fusion

Wang, Y., Liu, J., & Qian, G. (2024) · Progress in Nuclear Energy · doi:10.1016/j.pnucene.2024.105178 · Cited by 47

Metal–Organic Frameworks through the Lens of Artificial Intelligence: A Comprehensive Review

Neikha, K., & Пузари, А. (2024) · Langmuir · doi:10.1021/acs.langmuir.4c03126 · Cited by 53

Power Transformer Prognostics and Health Management Using Machine Learning: A Review and Future Directions

Zemouri, R. M. (2025) · Machines · doi:10.3390/machines13020125 · Cited by 57

GraphXAI: a survey of graph neural networks (GNNs) for explainable AI (XAI)

Nandan, M., Mitra, S., & De, D. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11054-3 · Cited by 46

Coherent Comments Generation for Chinese Articles with a Graph-to-Sequence Model

Li, W., Xu, J., He, Y., Yan, S., Wu, Y., & Sun, X. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1479 · Cited by 51

Optimization of Graph Neural Networks with Natural Gradient Descent

Izadi, M. R., Fang, Y., Stevenson, R., & Lin, L. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378063 · Cited by 51

A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation

Granados, C. A. B., Zanfei, A., Brentan, B., Montalvo, I., & Izquierdo, J. (2022) · Water · doi:10.3390/w14040514 · Cited by 46

Unsupervised strategies for identifying optimal parameters in Quantum Approximate Optimization Algorithm

Moussa, C., Wang, H., Bäck, T., & Dunjko, V. (2022) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-022-00131-4 · Cited by 55

On the Equivalence between Positional Node Embeddings and Structural Graph RepresentationsPreprint

Srinivasan, B., & Ribeiro, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.00452 · Cited by 73

Reliable Graph Neural Networks via Robust AggregationPreprint

Geisler, S., Zügner, D., & Günnemann, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.15651 · Cited by 46

A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Zhao, B.-W., You, Z., Hu, L., Guo, Z.-H., Wang, L., Chen, Z., & Wong, L. (2021) · Cancers · doi:10.3390/cancers13092111 · Cited by 45

Study-GNN: A Novel Pipeline for Student Performance Prediction Based on Multi-Topology Graph Neural Networks

Li, M., Wang, X., Wang, Y., Chen, Y., & Chen, Y. (2022) · Sustainability · doi:10.3390/su14137965 · Cited by 45

Transferable Graph Backdoor Attack

Yang, S., Doan, B. G., Montague, P., Vel, O. D., Abraham, T., Camtepe, S. A., Ranasinghe, D. C., & Kanhere, S. S. (2022) · International Symposium on Research in Attacks, Intrusions and Defenses (RAID) · doi:10.1145/3545948.3545976 · Cited by 48

CFGExplainer: Explaining Graph Neural Network-Based Malware Classification from Control Flow Graphs

Herath, J. D., Wakodikar, P., Yang, P., & Yan, G. (2022) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn53405.2022.00028 · Cited by 45

Hypergraph Transformer Neural Networks

Li, M., Zhang, Y., Li, X., Zhang, Y., & Yin, B. (2022) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3565028 · Cited by 47

Contrastive Multi-Modal Knowledge Graph Representation Learning

Fang, Q., Zhang, X., Hu, J., Wu, X., & Xu, C. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3220625 · Cited by 48

A survey of class-imbalanced semi-supervised learning

Gui, Q., Hong, Z., Guo, N., & Niu, B. (2023) · Machine Learning · doi:10.1007/s10994-023-06344-7 · Cited by 51

Graph neural networks for image‐guided disease diagnosis: A review

Zhang, L., Zhao, Y., Che, T., Li, S., & Wang, X. (2023) · iRadiology · doi:10.1002/ird3.20 · Cited by 53

Efficient deep data assimilation with sparse observations and time-varying sensors

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