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

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ImageNet classification with deep convolutional neural networks

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

Long Short-Term Memory

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Deep learning

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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173

Gradient-based learning applied to document recognition

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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

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Building powerful and equivariant graph neural networks with structural message-passingPreprint

Vignac, C., Loukas, A., & Frossard, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.15107 · Cited by 41

Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical StudiesPreprint

Jin, W., Li, Y., Xu, H., Wang, Y., Ji, S., Aggarwal, C. C., & Tang, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.00653 · Cited by 40

Graph Neural Networks: Methods, Applications, and OpportunitiesPreprint

Waikhom, L., & Patgiri, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.10733 · Cited by 40

Graph Neural Network for Spatiotemporal Data: Methods and ApplicationsPreprint

Li, Y., Yu, D., Liu, Z., Zhang, M., Gong, X., & Zhao, L. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4725185 · Cited by 57

Instance GNN: A Learning Framework for Joint Symbol Segmentation and Recognition in Online Handwritten Diagrams

Yun, X.-L., Zhang, Y., Yin, F., & Liu, C. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3087000 · Cited by 39

RLC-GNN: An Improved Deep Architecture for Spatial-Based Graph Neural Network with Application to Fraud Detection

Zeng, Y., & Tang, J. (2021) · Applied Sciences · doi:10.3390/app11125656 · Cited by 39

Dynamic Auto-Structuring Graph Neural Network: A Joint Learning Framework for Origin-Destination Demand Prediction

Zhang, D., & Xiao, F. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3135898 · Cited by 38

Higher-order Clustering and Pooling for Graph Neural Networks

Duval, A., & Malliaros, F. D. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557353 · Cited by 39

Trajectory prediction for autonomous driving based on multiscale spatial‐temporal graph

Tang, L., Yan, F., Zou, B., Li, W., Lv, C., & Wang, K. (2022) · IET Intelligent Transport Systems · doi:10.1049/itr2.12265 · Cited by 42

NeuralNEB—neural networks can find reaction paths fast

Schreiner, M., Bhowmik, A., Vegge, T., Jørgensen, P. B., & Winther, O. (2022) · Machine Learning Science and Technology · doi:10.1088/2632-2153/aca23e · Cited by 45

DualGCN: Exploring Syntactic and Semantic Information for Aspect-Based Sentiment Analysis

Li, R., Chen, H., Feng, F., Ma, Z., Wang, X., & Hovy, E. H. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3219615 · Cited by 43

Spatiotemporal Air Pollution Forecasting in Houston-TX: A Case Study for Ozone Using Deep Graph Neural Networks

Santos, V. O., Rocha, P. A. C., Scott, J., Thé, J. V. G., & Gharabaghi, B. (2023) · Atmosphere · doi:10.3390/atmos14020308 · Cited by 39

Leveraging physiology and artificial intelligence to deliver advancements in health care

Zhang, A., Wu, Z., Wu, E., Wu, M. A., Snyder, M. P., Zou, J., & Wu, J. C. (2023) · Physiological Reviews · doi:10.1152/physrev.00033.2022 · Cited by 44

Identifying Candidate Gene–Disease Associations via Graph Neural Networks

Cinaglia, P., & Cannataro, M. (2023) · Entropy · doi:10.3390/e25060909 · Cited by 39

A deep learning model with spatio-temporal graph convolutional networks for river water quality prediction

Huan, J., Liao, W., Zheng, Y., Xu, X., Zhang, H., & Shi, B. (2023) · Water Science & Technology Water Supply · doi:10.2166/ws.2023.164 · Cited by 42

A multisensory time-frequency features fusion method for rotating machinery fault diagnosis under nonstationary case

Liu, J., Xie, F., Zhang, Q., Lyu, Q., Wang, X., & Wu, S. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02198-x · Cited by 39

Artificial Intelligence and Machine Learning in Energy Conversion and Management

Mira, K., Bugiotti, F., & Morosuk, T. (2023) · Energies · doi:10.3390/en16237773 · Cited by 42

GANI: Global Attacks on Graph Neural Networks via Imperceptible Node Injections

Fang, J., Wen, H., Wu, J., Xuan, Q., Zheng, Z., & Tse, C. K. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3361027 · Cited by 42

Accelerating defect predictions in semiconductors using graph neural networks

Rahman, M. H., Gollapalli, P., Manganaris, P., Yadav, S. K., Pilania, G., DeCost, B., Choudhary, K. K., & Mannodi‐Kanakkithodi, A. (2024) · APL Machine Learning · doi:10.1063/5.0176333 · Cited by 39

Machine Learning in Petrology: State-of-the-Art and Future Perspectives

Petrelli, M. (2024) · Journal of Petrology · doi:10.1093/petrology/egae036 · Cited by 47

Solving Combinatorial Optimization Problems with Deep Neural Network: A Survey

Wang, F., He, Q., & Li, S. (2024) · Tsinghua Science & Technology · doi:10.26599/tst.2023.9010076 · Cited by 39

Graph refinement based airway extraction using mean-field networks and graph neural networks

Selvan, R., Kipf, T., Welling, M., Juárez, A. G.-U., Pedersen, J. H., Petersen, J., & de Bruijne, M. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101751 · Cited by 39

Developing graphical detection techniques for maintaining state estimation integrity against false data injection attack in integrated electric cyber-physical system

Li, Y., & Wang, Y. (2019) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2019.101705 · Cited by 39

Layer-wise relevance propagation of InteractionNet explains protein–ligand interactions at the atom level

Cho, H., Lee, E. K., & Choi, I. S. (2020) · Scientific Reports · doi:10.1038/s41598-020-78169-6 · Cited by 40

Railway Delay Prediction with Spatial-Temporal Graph Convolutional Networks

Heglund, J. S. W., Taleongpong, P., Hu, S., & Tran, H. (2020) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc45102.2020.9294742 · Cited by 41

Adversarial Caching Training: Unsupervised Inductive Network Representation Learning on Large-Scale Graphs

Chen, J., Gong, Z., Wang, W. W., Wang, C., Xu, Z., Lv, J., Li, X., Wu, K., & Liu, W. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3084195 · Cited by 45

Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

Lu, B., Gan, X., Jin, H., Fu, L., Wang, X., & Zhang, H. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3488902 · Cited by 40

A new approach for APT malware detection based on deep graph network for endpoint systems

Xuan, C. D., & Huong, D. (2022) · Applied Intelligence · doi:10.1007/s10489-021-03138-z · Cited by 39

A spatial temporal graph neural network model for predicting flashover in arbitrary building floorplans

Tam, W. C., Fu, E. Y., Li, J., Huang, X., Chen, J., & Huang, M. X. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105258 · Cited by 39

EdgePhase: A Deep Learning Model for Multi‐Station Seismic Phase Picking

Feng, T., Mohanna, S., & Meng, L. (2022) · Geochemistry Geophysics Geosystems · doi:10.1029/2022gc010453 · Cited by 45

Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networks

Jing, Y., Yang, Y., Wang, X., Song, M., & Tao, D. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00525 · Cited by 38

Exploring Gated Graph Sequence Neural Networks for Predicting Next Process Activities

Weinzierl, S. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-030-94343-1_3 · Cited by 38

Graph neural networks for conditional de novo drug design

Abate, C., Decherchi, S., & Cavalli, A. (2023) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1651 · Cited by 41

Sizing up feature descriptors for macromolecular machine learning with polymeric biomaterials

Stuart, S., Watchorn, J., & Gu, F. (2023) · npj Computational Materials · doi:10.1038/s41524-023-01040-5 · Cited by 44

Data-Driven Methodology for Knowledge Graph Generation Within the Tourism Domain

Chessa, A., Fenu, G., Motta, E., Osborne, F., Recupero, D. R., Salatino, A. A., & Secchi, L. (2023) · IEEE Access · doi:10.1109/access.2023.3292153 · Cited by 38

Graph-based deep learning techniques for remote sensing applications: Techniques, taxonomy, and applications — A comprehensive review

Khlifi, M. K., Boulila, W., & Farah, I. R. (2023) · Computer Science Review · doi:10.1016/j.cosrev.2023.100596 · Cited by 38

Dynamic link prediction by learning the representation of node-pair via graph neural networks

Dong, H., Li, L., Tian, D., Sun, Y., & Zhao, Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122685 · Cited by 39

DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization

Bhowmick, A., Kosan, M., Huang, Z., Singh, A. K., & Medya, S. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i10.28983 · Cited by 50

Graph neural networks for power grid operational risk assessment under evolving unit commitment

Zhang, Y., Karve, P., & Mahadevan, S. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124793 · Cited by 38

Deep learning: Historical overview from inception to actualization, models, applications and future trends

Ekundayo, O., & Ezugwu, A. E.-S. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113378 · Cited by 56

Image Emotion Distribution Learning with Graph Convolutional Networks

He, T., & Jin, X. (2019) · International Conference on Multimedia Retrieval · doi:10.1145/3323873.3326593 · Cited by 38

Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving

Hügle, M., Kalweit, G., Werling, M., & Boedecker, J. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197086 · Cited by 37

Multiple-Aspect Attentional Graph Neural Networks for Online Social Network User Localization

Zhong, T., Wang, T., Wang, J., Wu, J., & Zhou, F. (2020) · IEEE Access · doi:10.1109/access.2020.2993876 · Cited by 40

Deep Graph Normalizer: A Geometric Deep Learning Approach for Estimating Connectional Brain Templates

Gurbuz, M. B., & Rekik, I. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59728-3_16 · Cited by 38

Deep Fuzzy Graph Convolutional Networks for PolSAR Imagery Pixelwise Classification

Liu, H., Zhu, T., Shang, F., Liu, Y., Lv, D., & Yang, S. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3041534 · Cited by 38

Graph Convolutional Networks for the Automated Production of Building Vector Maps From Aerial Images

Wei, S., & Ji, S. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3060770 · Cited by 38

Efficient Data Loader for Fast Sampling-Based GNN Training on Large Graphs

Bai, Y., Li, C., Lin, Z., Wu, Y., Miao, Y., Liu, Y., & Xu, Y. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3065737 · Cited by 39

Low-Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

Hamrouni, A., Ghazzai, H., Alelyani, T., & Massoud, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3086410 · Cited by 38

Precise Learning of Source Code Contextual Semantics via Hierarchical Dependence Structure and Graph Attention Networks

Zhao, Z., Yang, B., Li, G., Liu, H., & Jin, Z. G. (2021) · Journal of Systems and Software · doi:10.1016/j.jss.2021.111108 · Cited by 38

SGCL

Shu, L., Du, E., Chang, Y., Chen, C., Zheng, Z., Xing, X., & Shen, S. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482478 · Cited by 40

Augmentation and heterogeneous graph neural network for AAAI2021-COVID-19 fake news detection

Karnyoto, A. S., Sun, C., Liu, B., & Wang, X. (2022) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-021-01503-5 · Cited by 38

Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows

Jawahar, P., Aarrestad, T. K., Chernyavskaya, N., Maurizio Pierini, B., Wozniak, K. A., Ngadiuba, J., Duarte, J. M., & Tsan, S. (2022) · Frontiers in Big Data · doi:10.3389/fdata.2022.803685 · Cited by 38

Prediction of GPCR activity using machine learning

Yadav, P., Mollaei, P., Cao, Z., Wang, Y., & Farimani, A. B. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.05.016 · Cited by 38

Emerging Planar Nanostructures Involving Both Local and Nonlocal Modes

Chai, R., Liu, Q., Liu, W., Li, Z., Cheng, H., Tian, J., & Chen, S. (2023) · ACS Photonics · doi:10.1021/acsphotonics.2c01534 · Cited by 38

Protein representations: Encoding biological information for machine learning in biocatalysis

Harding-Larsen, D., Funk, J., Madsen, N. G., Gharabli, H., Acevedo-Rocha, C. G., Mazurenko, S., & Welner, D. H. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108459 · Cited by 38

Dynamic Graph-Level Neural Network for SAR Image Change Detection

Wang, R., Wang, L., Wei, X., Chen, J., & Jiao, L. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2021.3132167 · Cited by 37

Fragment Linker Prediction Using the Deep Encoder-Decoder Network for PROTACs Drug Design

Kao, C., Lin, C., Chou, C.-L., & Lin, C. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01287 · Cited by 38

Highly accurate and large-scale collision cross sections prediction with graph neural networks

Guo, R., Zhang, Y., Liao, Y., Yang, Q., Xie, T., Fan, X., Lin, Z., Chen, Y., Lü, H., & Zhang, Z. (2023) · Communications Chemistry · doi:10.1038/s42004-023-00939-w · Cited by 37

Data-Driven Traffic Assignment: A Novel Approach for Learning Traffic Flow Patterns Using Graph Convolutional Neural Network

Rahman, M. R., & Hasan, S. (2023) · Data Science for Transportation · doi:10.1007/s42421-023-00073-y · Cited by 43

DeepCIP: A multimodal deep learning method for the prediction of internal ribosome entry sites of circRNAs

Zhou, Y., Wu, J., Yao, S., Yulian, X. U., Zhao, W., Tong, Y., & Zhou, Z. M. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107288 · Cited by 38

A review on semi-supervised learning for EEG-based emotion recognition

Qiu, S., Chen, Y., Yang, Y., Wang, P., Wang, Z., Zhao, H., Kang, Y., & Nie, R. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102190 · Cited by 37

Graph neural networks: a survey on the links between privacy and security

Guan, F., Zhu, T., Zhou, W., & Choo, K. R. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10656-4 · Cited by 47

A Comparative Review of AI Techniques for Automated Code Generation in Software Development: Advancements, Challenges, and Future Directions

Odeh, A. H., Odeh, N., & Mohammed, A. S. (2024) · TEM Journal · doi:10.18421/tem131-76 · Cited by 37

A GCN-based adaptive generative adversarial network model for short-term wind speed scenario prediction

Liu, X., Yu, J., Gong, L., Liu, M., & Xiang, X. (2024) · Energy · doi:10.1016/j.energy.2024.130931 · Cited by 37

Hierarchical Text Classification and Its Foundations: A Review of Current Research

Zangari, A., Marcuzzo, M., Rizzo, M., Giudice, L., Albarelli, A., & Gasparetto, A. (2024) · Electronics · doi:10.3390/electronics13071199 · Cited by 48

Predicting unsteady incompressible fluid dynamics with finite volume informed neural network

Li, T., Zou, S., Chang, X., Zhang, L., & Deng, X. (2024) · Physics of Fluids · doi:10.1063/5.0197425 · Cited by 37

DGSD: Dynamical graph self-distillation for EEG-based auditory spatial attention detection

Fan, C., Zhang, H., Huang, W. F., Xue, J., Tao, J., Yi, J., Lv, Z., & Wu, X. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106580 · Cited by 37

Survey on Graph Neural Networks

Gkarmpounis, G., Vranis, C., Vretos, N., & Daras, P. (2024) · IEEE Access · doi:10.1109/access.2024.3456913 · Cited by 43

Leveraging graph neural networks and multi-agent reinforcement learning for inventory control in supply chains

Kotecha, N., & Chanona, A. D. R. (2025) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2025.109111 · Cited by 52

When Machine Learning Meets Geospatial Data: A Comprehensive GeoAI Review

Boutayeb, A., Lahsen-Cherif, I., & Khadimi, A. E. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3568715 · Cited by 63

Automatic short answer grading by encoding student responses via a graph convolutional network

Tan, H., Wang, C., Duan, Q., Lu, Y., Hu, Z., & Li, R. (2020) · Interactive Learning Environments · doi:10.1080/10494820.2020.1855207 · Cited by 39

Feature Interactive Representation for Point Cloud Registration

Wu, B., Ma, J., Chen, G., & An, P. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00548 · Cited by 37

Identifying user geolocation with Hierarchical Graph Neural Networks and explainable fusion

Zhou, F., Wang, T., Zhong, T., & Trajcevski, G. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.11.004 · Cited by 38

Graph machine learning for design of high‐octane fuels

Rittig, J. G., Ritzert, M., Schweidtmann, A. M., Winkler, S., Weber, J. M., Morsch, P., Heufer, K. A., Grohe, M., Mitsos, A., & Dahmen, M. (2022) · AIChE Journal · doi:10.1002/aic.17971 · Cited by 37

Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural NetworksPreprint

Chen, Z., Chen, F., Zhang, L., Ji, T., Fu, K., Zhao, L., Chen, F., Wu, L., Aggarwal, C. C., & Lu, C.-T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.11867 · Cited by 49

Cell Complex Neural NetworksPreprint

Hajij, M., Istvan, K., & Zamzmi, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.00743 · Cited by 61

Automatic Fault Mapping in Remote Optical Images and Topographic Data With Deep Learning

Mattéo, L., Manighetti, I., Tarabalka, Y., Gaucel, J., van den Ende, M., Mercier, A., Taşar, O., Girard, N., Leclerc, F., Giampetro, T., et al. (2021) · Journal of Geophysical Research Solid Earth · doi:10.1029/2020jb021269 · Cited by 44

Heterogeneous graph neural network for attribute completion

Wang, K., Yu, Y., Huang, C., Zhao, Z., & Dong, J. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109171 · Cited by 36

Deep-Quantile-Regression-Based Surrogate Model for Joint Chance-Constrained Optimal Power Flow With Renewable Generation

Chen, G., Zhang, H., Hui, H., & Song, Y. (2022) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2022.3223764 · Cited by 36

Context-Aware Neural Fault Localization

Zhang, Z., Lei, Y., Mao, X., Yan, M. L., Xia, X., & Lo, D. (2023) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2023.3279125 · Cited by 38

Fusing modalities by multiplexed graph neural networks for outcome prediction from medical data and beyond

D’Souza, N. S., Wang, H., Giovannini, A. A., Foncubierta–Rodríguez, A., Beck, K. L., Boyko, O. B., & Syeda-Mahmood, T. F. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103064 · Cited by 36

Graph neural networks in vision-language image understanding: a survey

Senior, H., Slabaugh, G., Yuan, S., & Rossi, L. (2024) · The Visual Computer · doi:10.1007/s00371-024-03343-0 · Cited by 41

An interpretable graph convolutional neural network based fault diagnosis method for building energy systems

Li, G., Yao, Z., Chen, L., Li, T., & Xu, C. (2024) · Building Simulation · doi:10.1007/s12273-024-1125-6 · Cited by 36

Beyond Grid Data: Exploring graph neural networks for Earth observation

Zhao, S., Chen, Z., Xiong, Z., Shi, Y.-L., Saha, S., & Zhu, X. X. (2024) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2024.3493972 · Cited by 48

Deeper Insights Into Deep Graph Convolutional Networks: Stability and Generalization

Yang, G., Li, M., Han, F., & Zhuang, X. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3616350 · Cited by 36

Syntax-aware Multi-task Graph Convolutional Networks for Biomedical Relation Extraction

Li, D., & Ji, H. (2019) · International Workshop on Health Text Mining and Information Analysis (LOUHI) · doi:10.18653/v1/d19-6204 · Cited by 36

GINNs: Graph-Informed Neural Networks for multiscale physics

Hall, E. J., Taverniers, S., Katsoulakis, M. A., & Tartakovsky, D. M. (2021) · Journal of Computational Physics · doi:10.1016/j.jcp.2021.110192 · Cited by 38

GraphMDN: Leveraging graph structure and deep learning to solve inverse problems

Oikarinen, T., Hannah, D., & Kazerounian, S. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534301 · Cited by 37

MobileGCN applied to low-dimensional node feature learning

Dong, W., Wu, J., Bai, Z., Hu, Y., Li, W., Qiao, W., & Woźniak, M. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107788 · Cited by 38

Affordance-Based Grasping Point Detection Using Graph Convolutional Networks for Industrial Bin-Picking Applications

Iriondo, A., Lazkano, E., & Ansuategi, A. (2021) · Sensors · doi:10.3390/s21030816 · Cited by 36

Network of Tensor Time Series

Jing, B., Tong, H., & Zhu, Y. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449969 · Cited by 39

A convolutional strategy on unstructured mesh for the adjoint vector modeling

Xu, M., Song, S., Sun, X., & Zhang, W. (2021) · Physics of Fluids · doi:10.1063/5.0044093 · Cited by 38

Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift

Liu, H., Hu, B., Wang, X., Shi, C., Zhang, Z., & Zhou, J. W. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3512172 · Cited by 36

Modular Multi–Source Prediction of Drug Side–Effects With DruGNN

Bongini, P., Scarselli, F., Bianchini, M., Dimitri, G. M., Pancino, N., & Lió, P. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3175362 · Cited by 36

Generalized Simultaneous Localization and Mapping (G-SLAM) as unification framework for natural and artificial intelligences: towards reverse engineering the hippocampal/entorhinal system and principles of high-level cognition

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