Graph convolutional networks: a comprehensive review

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A Comprehensive Survey on Graph Neural Networks

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Dynamic Graph CNN for Learning on Point Clouds

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Graph neural networks: A review of methods and applications

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Neural Message Passing for Quantum ChemistryPreprint

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Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

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Variational Graph Auto-EncodersPreprint

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Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

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Neural Graph Collaborative Filtering

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Graph Convolutional Neural Networks for Web-Scale Recommender Systems

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Geometric Deep Learning: Going beyond Euclidean data

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PointNet: Deep Learning on Point Sets for 3D Classification and SegmentationPreprint

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Gated Graph Sequence Neural NetworksPreprint

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A Comprehensive Survey on Graph Neural Networks

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Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning

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Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

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Convolutional Networks on Graphs for Learning Molecular Fingerprints

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Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic ForecastingPreprint

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Learning Convolutional Neural Networks for GraphsPreprint

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Graph Convolutional Networks for Text Classification

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Modeling polypharmacy side effects with graph convolutional networks

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Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs

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Detecting Visual Relationships with Deep Relational Networks

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SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels

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Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN

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Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation

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A Convolutional Encoder Model for Neural Machine Translation

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Graph Convolutional Networks With Argument-Aware Pooling for Event Detection

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Fake news detection on social media using geometric deep learning

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Rethinking Knowledge Graph Propagation for Zero-Shot Learning

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RGCNN: Regularized Graph CNN for Point Cloud Segmentation

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Graph Classification using Structural Attention

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Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks

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Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks

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Deformable Shape Completion with Graph Convolutional Autoencoders

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Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks

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Flexible Neural Representation for Physics PredictionPreprint

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Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question AnsweringPreprint

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LanczosNet: Multi-Scale Deep Graph Convolutional NetworksPreprint

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Universal Invariant and Equivariant Graph Neural NetworksPreprint

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Provably Powerful Graph NetworksPreprint

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Stochastic Training of Graph Convolutional Networks with Variance Reduction

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Learning Localized Generative Models for 3D Point Clouds via Graph Convolution

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RGCNN: Regularized Graph CNN for Point Cloud SegmentationPreprint

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GraphIE: A Graph-Based Framework for Information ExtractionPreprint

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Graph Neural Networks for IceCube Signal Classification

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DeepChemStable: Chemical Stability Prediction with an Attention-Based Graph Convolution Network

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HiDDen: Hierarchical Dense Subgraph Detection with Application to Financial Fraud Detection

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A Local Algorithm for Structure-Preserving Graph Cut

Zhou, D., Zhang, S., Yildirim, M. Y., Alcorn, S., Tong, H., Davulcu, H., & He, J. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098015 · Cited by 58

Context-Dependent Diffusion Network for Visual Relationship Detection

Cui, Z., Xu, C., Zheng, W., & Yang, J. J. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240668 · Cited by 53

Community Detection with Graph Neural NetworksPreprint

Bruna, J., & Li, X. (2017) · arXiv (Cornell University) · Cited by 52

Neural Message Passing for Jet Physics

Henrion, I., Brehmer, J., Bruna, J., Cho, K., Cranmer, K. S., Louppe, G., & Rochette, G. (2017) · ORBi (University of Liège) · Cited by 47

Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations

Gao, H., Chen, Y., & Ji, S. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313395 · Cited by 44

Dual graph convolutional neural network for predicting chemical networks

Harada, S., Akita, H., Tsubaki, M., Baba, Y., Takigawa, I., Yamanishi, Y., & Kashima, H. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3378-0 · Cited by 43

Graph Neural Networks for IceCube Signal ClassificationPreprint

Choma, N., Monti, F., Gerhardt, L., Palczewski, T. J., Ronaghi, Z., Prabhat, Bhimji, W., Bronstein, M. M., Klein, S. R., & Bruna, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.06166 · Cited by 34

Neighbors Do Help: Deeply Exploiting Local Structures of Point Clouds

Shen, Y., Feng, C., Yang, Y. L., & Tian, D. (2017) · Cited by 24

Forecasting Retweet Count during Elections Using Graph Convolution Neural Networks

Vijayan, R., & Mohler, G. (2018) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2018.00036 · Cited by 13

Dependency Parsing with Dilated Iterated Graph CNNs

Strubell, E., & McCallum, A. (2017) · SPNLP@EMNLP · doi:10.18653/v1/w17-4301 · Cited by 10

Graph neural networks: A review of methods and applications

Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020) · AI Open · doi:10.1016/j.aiopen.2021.01.001 · Cited by 6,880

Deep Learning on Graphs: A Survey

Zhang, Z., Cui, P., & Zhu, W. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2981333 · Cited by 1,822

A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

Alzubaidi, L., Bai, J., Al-Sabaawi, A., Santamaría, J., Albahri, A. S., Al-dabbagh, B. S. N., Fadhel, M. A., Manoufali, M., Zhang, J., Al-Timemy, A. H., et al. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00727-2 · Cited by 1,057

Graph neural networks for materials science and chemistry

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Artificial intelligence for multimodal data integration in oncology

Lipkova, J., Chen, R. J., Chen, B., Lu, M. Y., Barbieri, M., Shao, D., Vaidya, A. J., Chen, C., Zhuang, L., Williamson, D. F. K., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.09.012 · Cited by 777

WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation

Jeong, J., Zou, Y., Kim, T., Zhang, D., Ravichandran, A., & Dabeer, O. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01878 · Cited by 585

Beyond Homophily in Graph Neural Networks: Current Limitations and Effective DesignsPreprint

Zhu, J., Yan, Y., Zhao, L., Heimann, M., Akoglu, L., & Koutra, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.11468 · Cited by 512

Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence

Bhatti, U. A., Tang, H., Wu, G., Marjan, S., & Hussain, A. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/8342104 · Cited by 502

A review of deep learning techniques for speech processing

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Graph Neural Networks for Intelligent Transportation Systems: A Survey

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Autonomous driving system: A comprehensive survey

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A gentle introduction to deep learning for graphs

Bacciu, D., Errica, F., Micheli, A., & Podda, M. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.06.006 · Cited by 327

How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey

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Spatial-temporal graph neural network for traffic forecasting: An overview and open research issues

Bui, K. N., Cho, J.-H., & Yi, H. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02587-w · Cited by 293

Edge Intelligence in Intelligent Transportation Systems: A Survey

Gong, T., Zhu, L., Yu, F. R., & Tang, T. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3275741 · Cited by 297

A Survey of Information Cascade Analysis

Zhou, F., Xu, X., Trajcevski, G., & Zhang, K. (2021) · ACM Computing Surveys · doi:10.1145/3433000 · Cited by 281

A review on deep learning in planetary gearbox health state recognition: methods, applications, and dataset publication

Liu, D., Cui, L., & Cheng, W. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/acf390 · Cited by 272

Graph Neural Network: A Comprehensive Review on Non-Euclidean Space

Asif, N. A., Sarker, Y., Chakrabortty, R. K., Ryan, M. J., Ahamed, M. H., Saha, D. K., Badal, F. R., Das, S. K., Ali, M. F., Moyeen, S. I., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3071274 · Cited by 241

A Survey on Hypergraph Representation Learning

Antelmi, A., Cordasco, G., Polato, M., Scarano, V., Spagnuolo, C., & Yang, D. (2023) · ACM Computing Surveys · doi:10.1145/3605776 · Cited by 267

Emotion recognition in EEG signals using deep learning methods: A review

Jafari, M., Shoeibi, A., Khodatars, M., Bagherzadeh, S., Shalbaf, A., García, D. L., Gorriz, J. M., & Acharya, U. R. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107450 · Cited by 240

Towards Self-X cognitive manufacturing network: An industrial knowledge graph-based multi-agent reinforcement learning approach

Zheng, P., Xia, L., Li, C., Li, X., & Liu, B. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.08.002 · Cited by 213

Data Augmentation for Deep Graph Learning

Ding, K., Xu, Z., Tong, H., & Liu, H. (2022) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3575637.3575646 · Cited by 271

Modulated Graph Convolutional Network for 3D Human Pose Estimation

Zou, Z., & Tang, W. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01128 · Cited by 203

Multi-View Graph Convolutional Network for Multimedia Recommendation

Yu, P., Tan, Z., Lu, G., & Bao, B. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3613915 · Cited by 195

Deep learning for Alzheimer's disease diagnosis: A survey

Khojaste-Sarakhsi, M., Haghighi, S. S., Ghomi, S. M. T. F., & Marchiori, E. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102332 · Cited by 194

Protein–protein interaction prediction with deep learning: A comprehensive review

Soleymani, F., Paquet, E., Viktor, H. L., Michalowski, W., & Spinello, D. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.08.070 · Cited by 209

Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review

Shoeibi, A., Khodatars, M., Jafari, M., Moridian, P., Rezaei, M., Alizadehsani, R., Khozeimeh, F., Górriz, J. M., Heras, J., Panahiazar, M., et al. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104697 · Cited by 200

Machine Learning in Polymer Research

Ge, W. C., Silva, R. D., Fan, Y., Sisson, S. A., & Stenzel, M. H. (2025) · Advanced Materials · doi:10.1002/adma.202413695 · Cited by 202

Deep learning in food authenticity: Recent advances and future trends

Deng, Z., Wang, T., Zheng, Y., Zhang, W., & Yun, Y. (2024) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2024.104344 · Cited by 183

HDMI: High-order Deep Multiplex Infomax

Jing, B., Park, C., & Tong, H. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449971 · Cited by 175

Learning Combinatorial Optimization on Graphs: A Survey With Applications to Networking

Vassileva, N., Steinert, R., Pérez-Ramírez, D. F., & Boman, M. (2020) · IEEE Access · doi:10.1109/access.2020.3004964 · Cited by 178

A survey of multimodal hybrid deep learning for computer vision: Architectures, applications, trends, and challenges

Bayoudh, K. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102217 · Cited by 180

Graph Deep Learning Model for Mapping Mineral Prospectivity

Zuo, R., & Xu, Y. (2022) · Mathematical Geosciences · doi:10.1007/s11004-022-10015-z · Cited by 172

Fake news detection: A survey of graph neural network methods

Phan, H. T., Nguyên, N. T., & Hwang, D. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110235 · Cited by 178

Attention based spatiotemporal graph attention networks for traffic flow forecasting

Wang, Y., Jing, C., Xu, S., & Guo, T. (2022) · Information Sciences · doi:10.1016/j.ins.2022.05.127 · Cited by 159

Graph Convolutional Neural Network for Human Action Recognition: A Comprehensive Survey

Ahmad, T., Jin, L., Zhang, X., Lai, S., Tang, G., & Lin, L. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3076974 · Cited by 159

A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD

Zhao, K., Duka, B., Xie, H., Oathes, D. J., Calhoun, V. D., & Zhang, Y. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118774 · Cited by 150

AI-Driven Deep Learning Techniques in Protein Structure Prediction

Chen, L., Li, Q., Nasif, K. F. A., Xie, Y., Deng, B., Niu, S., Pouriyeh, S., Dai, Z., Chen, J., & Xie, Y. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25158426 · Cited by 152

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

Herrera, M., Hernández, M. P., Parlikad, A. K., & Izquierdo, J. (2020) · Processes · doi:10.3390/pr8030312 · Cited by 143

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

Data-driven multi-output prediction for TBM performance during tunnel excavation: An attention-based graph convolutional network approach

Pan, Y., Fu, X., & Zhang, L. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104386 · Cited by 124

Updated review of advances in microRNAs and complex diseases: taxonomy, trends and challenges of computational models

Li, H., Zhang, L., & Chen, X. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac358 · Cited by 123

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

Data-driven stock forecasting models based on neural networks: A review

Bao, W., Cao, Y., Yang, Y., Che, H., Huang, J., & Wen, S. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102616 · Cited by 166

Data‐Driven High‐Throughput Rational Design of Double‐Atom Catalysts for Oxygen Evolution and Reduction

Wu, L., Guo, T., & Li, T. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202203439 · Cited by 115

InFoRM: Individual Fairness on Graph Mining

Kang, J. L., He, J., Maciejewski, R., & Tong, H. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403080 · Cited by 113

RA-GCN: Graph convolutional network for disease prediction problems with imbalanced data

Ghorbani, M., Kazi, A., Baghshah, M. S., Rabiee, H. R., & Navab, N. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102272 · Cited by 118

Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review

Jafari, M., Shoeibi, A., Khodatars, M., Ghassemi, N., Moridian, P., Alizadehsani, R., Khosravi, A., Ling, S. H., Delfan, N., Zhang, Y., et al. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106998 · Cited by 124

Aspect-level sentiment analysis: A survey of graph convolutional network methods

Phan, H. T., Nguyên, N. T., & Hwang, D. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.10.004 · Cited by 115

Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review

Shoeibi, A., Khodatars, M., Jafari, M., Ghassemi, N., Moridian, P., Alizadehsani, R., Ling, S. H., Khosravi, A., Alinejad‐Rokny, H., Lam, H., et al. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.12.010 · Cited by 110

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

Convolutional Neural Networks or Vision Transformers: Who Will Win the Race for Action Recognitions in Visual Data?

Moutik, O., Sekkat, H., Tigani, S., Chehri, A., Saadane, R., Tchakoucht, T. A., & Paul, A. (2023) · Sensors · doi:10.3390/s23020734 · Cited by 114

Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies

Xue, P., Tang, C., Li, Q., Li, Y., Shen, Y., Zhao, Y., Chen, J., Wu, J., Li, L., Wang, W., et al. (2020) · BMC Medicine · doi:10.1186/s12916-020-01860-y · Cited by 102

Deep anomaly detection in horizontal axis wind turbines using Graph Convolutional Autoencoders for Multivariate Time series

Miele, E. S., Bonacina, F., & Corsini, A. (2022) · Energy and AI · doi:10.1016/j.egyai.2022.100145 · Cited by 110

A User’s Guide to Machine Learning for Polymeric Biomaterials

Meyer, T. A., Ramirez, C., Tamasi, M., & Gormley, A. J. (2022) · ACS Polymers Au · doi:10.1021/acspolymersau.2c00037 · Cited by 106

Knowledge and topology: A two layer spatially dependent graph neural networks to identify urban functions with time-series street view image

Zhang, Y., Liu, P., & Biljecki, F. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.03.008 · Cited by 107

Region-Level Traffic Prediction Based on Temporal Multi-Spatial Dependence Graph Convolutional Network from GPS Data

Yang, H., Zhang, X., Li, Z., & Cui, J. (2022) · Remote Sensing · doi:10.3390/rs14020303 · Cited by 97

Deep learning for cybersecurity in smart grids: Review and perspectives

Ruan, J., Liang, G., Zhao, J., Zhao, H. Q., Qiu, J., Wen, F., & Dong, Z. Y. (2023) · Energy Conversion and Economics · doi:10.1049/enc2.12091 · Cited by 111

A novel EEG-based graph convolution network for depression detection: Incorporating secondary subject partitioning and attention mechanism

Zhang, Z., Meng, Q., Jin, L.-C., Wang, H.-G., & Hou, H.-R. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122356 · Cited by 90

A high-accuracy and lightweight detector based on a graph convolution network for strip surface defect detection

Wang, G.-Q., Zhang, C., Chen, M.-S., Lin, Y. C., Tan, X.-H., Kang, Y., Wang, Q., Zeng, W., & Zhao, W. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102280 · Cited by 91

A deep learning method for repurposing antiviral drugs against new viruses via multi-view nonnegative matrix factorization and its application to SARS-CoV-2

Su, X., Hu, L., You, Z., Hu, P., Wang, L., & Zhao, B.-W. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab526 · Cited by 88

Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review

Lee, M. (2023) · Molecules · doi:10.3390/molecules28135169 · Cited by 97

Graph neural networks for text classification: a survey

Wang, K., Ding, Y., & Han, S. C. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10808-0 · Cited by 106

PND-Net: plant nutrition deficiency and disease classification using graph convolutional network

Bera, A., Bhattacharjee, D., & Krejcar, O. (2024) · Scientific Reports · doi:10.1038/s41598-024-66543-7 · Cited by 87

Encrypted Malware Traffic Detection via Graph-based Network Analysis

Fu, Z., Liu, M., Qin, Y., Zhang, J., Zou, Y., Yin, Q., Li, Q. L., & Duan, H. (2022) · International Symposium on Research in Attacks, Intrusions and Defenses (RAID) · doi:10.1145/3545948.3545983 · Cited by 91

Data-driven real-time advanced geological prediction in tunnel construction using a hybrid deep learning approach

Fu, X., Wu, M., Tiong, R. L. K., & Zhang, L. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104672 · Cited by 89

Variable Correlation Analysis-Based Convolutional Neural Network for Far Topological Feature Extraction and Industrial Predictive Modeling

Yuan, X., Wang, Y., Wang, C., Ye, L., Wang, K., Wang, Y., Yang, C., Gui, W., & Shen, F. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3373085 · Cited by 91

Spatial transformer network on skeleton‐based gait recognition

Zhang, C., Chen, X., Han, G., & Liu, X. (2022) · Expert Syst. J. Knowl. Eng. · doi:10.1111/exsy.13244 · Cited by 100

Deep graph convolutional reinforcement learning for financial portfolio management – DeepPocket

Soleymani, F., & Paquet, E. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115127 · Cited by 93

Survey on Machine Learning for Traffic-Driven Service Provisioning in Optical Networks

Panayiotou, T., Michalopoulou, M., & Ellinas, G. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3247842 · Cited by 88

A review of non-rigid transformations and learning-based 3D point cloud registration methods

Monji-Azad, S., Hesser, J., & Löw, N. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.12.023 · Cited by 85

A Novel Early Detection and Prevention of Coronary Heart Disease Framework Using Hybrid Deep Learning Model and Neural Fuzzy Inference System

Ramesh, B., & Lakshmanna, K. (2024) · IEEE Access · doi:10.1109/access.2024.3366537 · Cited by 88

MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph

Chu, Y., Wang, X., Dai, Q., Wang, Y., Wang, Q., Peng, S., Wei, X.-Y., Qiu, J., Salahub, D. R., Xiong, Y., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab165 · Cited by 80

A Comprehensive Survey on Multi-Agent Reinforcement Learning for Connected and Automated Vehicles

Yadav, P., Mishra, A., & Kim, S. (2023) · Sensors · doi:10.3390/s23104710 · Cited by 84

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

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

GTR-GA: Harnessing the power of graph-based neural networks and genetic algorithms for text augmentation

Onan, A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120908 · Cited by 77

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

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

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

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

Graph convolutional networks in language and vision: A survey

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

Deep hypergraph autoencoder embedding: An efficient intelligent approach for rotating machinery fault diagnosis

Shi, M., Ding, C., Wang, R., Song, Q., Shen, C., Huang, W., & Zhu, Z. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110172 · Cited by 74

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

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

LSTM-based graph attention network for vehicle trajectory prediction

Wang, J., Liu, K., & Li, H. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110477 · Cited by 74

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

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

Application of Transformers in Cheminformatics

Luong, K.-D., & Singh, A. K. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c02070 · Cited by 76

Network traffic analysis through node behaviour classification: a graph-based approach with temporal dissection and data-level preprocessing

Zola, F., Segurola-Gil, L., Bruse, J. L., Galar, M., & Orduna-Urrutia, R. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102632 · Cited by 79

Aero-Engine Blade Defect Detection: A Systematic Review of Deep Learning Models

Abdulrahman, Y., Eltoum, M. A. M., Ayyad, A., Moyo, B., & Zweiri, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3280992 · Cited by 74

Cross-Domain Recommendation via Progressive Structural Alignment

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

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

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

Directed Graph Convolutional NetworkPreprint

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Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis

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Graph Attention Network With Spatial-Temporal Clustering for Traffic Flow Forecasting in Intelligent Transportation System

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Graph Signal Processing and Deep Learning: Convolution, Pooling, and Topology

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When Machine Learning and Deep Learning Come to the Big Data in Food Chemistry

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Graph neural networks for construction applications

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An improved multi-channel graph convolutional network and its applications for rotating machinery diagnosis

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Graph Convolutional Networks: Algorithms, Applications and Open Challenges

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A comprehensive review of graph convolutional networks: approaches and applications

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Diffusion convolution recurrent neural network – a comprehensive survey

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Graph convolutional networks

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