Geometric Deep Learning: Going beyond Euclidean data

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

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

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

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

Untitled work

(2008) · Journal of Machine Learning Research · Cited by 33,051

Efficient Estimation of Word Representations in Vector Space

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568

Semi-supervised classification with graph convolutional networks

Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853

Distributed Representations of Words and Phrases and their Compositionality

Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533

Sequence to Sequence Learning with Neural Networks

Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766

A Wavelet Tour of Signal Processing

Mallat, S. (1999) · Elsevier eBooks · doi:10.1016/b978-0-12-466606-1.x5000-4 · Cited by 16,329

Unsupervised deep learning of landscape typologies from remote sensing images and other continuous spatial data

van Strien, M. J., & Grêt‐Regamey, A. (2022) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2022.105462 · Cited by 25

HCNA: Hyperbolic Contrastive Learning Framework for Self-Supervised Network Alignment

Saxena, S., Chakraborty, R., & Chandra, J. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103021 · Cited by 25

Graph Neural Networks and Open-Government Data to Forecast Traffic Flow

Brimos, P., Karamanou, A., Kalampokis, E., & Tarabanis, K. A. (2023) · Information · doi:10.3390/info14040228 · Cited by 28

Multi-view expressive graph neural networks for 3D CAD model classification

Li, S., & Corney, J. (2023) · Computers in Industry · doi:10.1016/j.compind.2023.103993 · Cited by 26

Multi-modal Disease Classification in Incomplete Datasets Using Geometric Matrix Completion

Vivar, G., Zwergal, A., Navab, N., & Ahmadi, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00689-1_3 · Cited by 25

SEMANTIC URBAN MESH ENHANCEMENT UTILIZING A HYBRID MODEL

Tutzauer, P., Laupheimer, D., & Norbert Haala, A. (2019) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iv-2-w7-175-2019 · Cited by 24

Signal Processing on Simplicial Complexes

Schaub, M. T., Seby, J.-B., Frantzen, F., Roddenberry, T. M., Zhu, Y., & Segarra, S. (2022) · Understanding complex systems · doi:10.1007/978-3-030-91374-8_12 · Cited by 25

Deep learning algorithms applied to computational chemistry

Pando, A. G., Ramírez-Alonso, G. M. D. J., Arzate-Quintana, C., & Camarillo‐Cisneros, J. (2023) · Molecular Diversity · doi:10.1007/s11030-023-10771-y · Cited by 28

Graph pooling in graph neural networks: methods and their applications in omics studies

Wang, Y., Hou, W., Sheng, N., Zhao, Z., Liu, J., Huang, L., & Wang, J. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10918-9 · Cited by 24

On the Efficiency of a Point Cloud Autoencoder as a Geometric Representation for Shape Optimization

Rios, T., Sendhoff, B., Menzel, S., Bäck, T., & van Stein, B. (2019) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci44817.2019.9003161 · Cited by 24

Performance Tuning of Spade Card Antenna using Mean Average Loss of Backpropagation Neural Network

Mujahidin, I., Arman, D., Nachrowie, Aji, S., & Surya, P. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110280 · Cited by 24

From Local to Global Semantic Clone Detection

Yuan, Y., Kong, W., Hou, G., Hu, Y. Z., Watanabe, M., & Fukuda, A. (2020) · International Conference on Dependable Systems and Their Applications (DSA) · doi:10.1109/dsa.2019.00012 · Cited by 24

Gaussian Processes on Graphs Via Spectral Kernel Learning

Zhi, Y.-C., Ng, Y. C., & Dong, X. (2023) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2023.3265160 · Cited by 24

FGCN: Deep Feature-based Graph Convolutional Network for Semantic Segmentation of Urban 3D Point Clouds

Khan, S., Shi, Y., Shahzad, M., & Zhu, X. X. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00107 · Cited by 24

Graph convolutional and attention models for entity classification in multilayer networks

Zangari, L., Interdonato, R., Caliò, A., & Tagarelli, A. (2021) · Applied Network Science · doi:10.1007/s41109-021-00420-4 · Cited by 25

CarHoods10k: An Industry-Grade Data Set for Representation Learning and Design Optimization in Engineering Applications

Wollstadt, P., Bujny, M., Ramnath, S., Shah, J. J., Detwiler, D. T., & Menzel, S. (2022) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2022.3147013 · Cited by 24

Link Prediction in Time Varying Social Networks

Carchiolo, V., Cavallo, C., Grassia, M., Malgeri, M., & Mangioni, G. (2022) · Information · doi:10.3390/info13030123 · Cited by 24

CancerOmicsNet: a multi-omics network-based approach to anti-cancer drug profiling

Pu, L., Singha, M., Ramanujam, J., & Bryliński, M. (2022) · Oncotarget · doi:10.18632/oncotarget.28234 · Cited by 25

E-minBatch GraphSAGE: An Industrial Internet Attack Detection Model

Lan, J., Lu, J., Wan, G., Wang, Y. Y., Huang, C. Y., Zhang, S. B., Huang, Y., & Jin, N. (2022) · Security and Communication Networks · doi:10.1155/2022/5363764 · Cited by 24

Multi-view Pixel2Mesh++: 3D reconstruction via Pixel2Mesh with more images

Chen, R., Yin, X., Yang, Y., & Tong, C. (2022) · The Visual Computer · doi:10.1007/s00371-022-02651-7 · Cited by 26

Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation

Guo, N., Liu, X., Li, S., Ma, Q., Gao, K., Han, B., Zheng, L., Guo, S., & Guo, X. (2023) · ACM Transactions on Information Systems · doi:10.1145/3568395 · Cited by 24

Exploring attention mechanism for graph similarity learning

Tan, W., Gao, X., Li, Y., Wen, G., Cao, P., Yang, J., Li, W., & Zai͏̈ane, O. R. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110739 · Cited by 24

Understanding and Improving Features Learned in Deep Functional Maps

Attaiki, S., & Ovsjanikov, M. (2023) · 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/cvpr52729.2023.00133 · Cited by 24

Graph Neural Networks for the Prediction of Molecular Structure–Property Relationships

Rittig, J. G., Gao, Q., Dahmen, M., Mitsos, A., & Schweidtmann, A. M. (2023) · Royal Society of Chemistry eBooks · doi:10.1039/bk9781837670178-00159 · Cited by 33

Edge-labeling based modified gated graph network for few-shot learning

Zheng, P., Guo, X., Chen, E., Qi, L., & Guan, L. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110264 · Cited by 25

Generalized Simplicial Attention Neural Networks

Battiloro, C., Testa, L., Giusti, L., Sardellitti, S., Lorenzo, P. D., & Barbarossa, S. (2024) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2024.3485473 · Cited by 24

Local-Aggregation Graph Networks

Chang, J., Wang, L., Meng, G., Zhang, Q., Xiang, S., & Pan, C. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2915591 · Cited by 25

Integrating 3D Geometry of Organ for Improving Medical Image Segmentation

Yao, J., Cai, J., Yang, D., Xu, D., & Huang, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32254-0_36 · Cited by 24

Intrinsic and extrinsic operators for shape analysis

Wang, Y., & Solomon, J. (2019) · Handbook of numerical analysis · doi:10.1016/bs.hna.2019.08.003 · Cited by 23

MvsGCN

Wu, J., Zhong, S.-H., & Liu, Y. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3350938 · Cited by 24

Embedding high-dimensional Bayesian optimization via generative modeling: Parameter personalization of cardiac electrophysiological models

Dhamala, J., Bajracharya, P., Arevalo, H., Sapp, J. L., Horáček, B. M., Wu, K. C., Trayanova, N. A., & Wang, L. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101670 · Cited by 24

FWNet: Semantic Segmentation for Full-Waveform LiDAR Data Using Deep Learning

Shinohara, T., Xiu, H., & Matsuoka, M. (2020) · Sensors · doi:10.3390/s20123568 · Cited by 27

A Novel Local Geometry Capture in Pointnet++ for 3D Classification

Sheshappanavar, S. V., & Kambhamettu, C. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00139 · Cited by 24

Graph Neural Networks Using Local Descriptions in Attributed Graphs: An Application to Symbol Recognition and Hand Written Character Recognition

Kajla, N. I., Missen, M. M. S., Luqman, M. M., & Coustaty, M. (2021) · IEEE Access · doi:10.1109/access.2021.3096845 · Cited by 24

ncRNA Classification with Graph Convolutional NetworksPreprint

Rossi, E., Monti, F., Bronstein, M. M., & Lió, P. (2019) · arXiv (Cornell University) · doi:10.17863/cam.41991 · Cited by 39

Learning Generalizable Device Placement Algorithms for Distributed Machine Learning

Addanki, R., Venkatakrishnan, S. B., Gupta, S., Mao, H., & Alizadeh, M. (2019) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 24

Cross-Domain Few-Shot Learning with Meta Fine-TuningPreprint

Cai, J., & Sheng, Q. Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.10544 · Cited by 24

Track Seeding and Labelling with Embedded-space Graph Neural NetworksPreprint

Choma, N., Murnane, D., Ju, X., Calafiura, P., Conlon, S., Farrell, S., Prabhat, Cerati, G. B., Gray, L., Klijnsma, T., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.00149 · Cited by 24

MolCLR: Molecular Contrastive Learning of Representations via Graph Neural NetworksPreprint

Wang, Y., Wang, J., Cao, Z., & Farimani, A. B. (2021) · arXiv (Cornell University) · doi:10.24433/co.8582800.v1 · Cited by 24

PathML: A unified framework for whole-slide image analysis with deep learningPreprint

Berman, A. G., Orchard, W. R., Gehrung, M., & Markowetz, F. M. (2021) · medRxiv · doi:10.1101/2021.07.07.21260138 · Cited by 26

A visual introduction to Gaussian Belief PropagationPreprint

Ortiz, J., Evans, T., & Davison, A. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.02308 · Cited by 24

Editor’s Comments

Padmanabhan, B., Fang, X., Sahoo, N., & Burton‐Jones, A. (2022) · MIS Quarterly · doi:10.25300/misq/2022/461e1 · Cited by 28

Assessing progressive collapse regions of reinforced concrete frame structures using Graph Convolutional Networks

Wang, S., Cheng, X., Li, Y., Yang, X., Zhang, H., Guo, R., Song, X., & Liang, Z. (2024) · Engineering Structures · doi:10.1016/j.engstruct.2024.119076 · Cited by 24

Machine learning to predict de novo protein–protein interactions

Gaínza, P., Bunker, R. D., Townson, S. A., & Castle, J. C. (2025) · Trends in biotechnology · doi:10.1016/j.tibtech.2025.04.013 · Cited by 26

To Identify Tree Species With Highly Similar Leaves Based on a Novel Attention Mechanism for CNN

Song, Y., He, F., & Zhang, X. (2019) · IEEE Access · doi:10.1109/access.2019.2951607 · Cited by 23

ZerNet: Convolutional Neural Networks on Arbitrary Surfaces Via Zernike Local Tangent Space Estimation

Sun, Z., Rooke, E., Charton, J., He, Y., Lu, J., & Baek, S. (2020) · Computer Graphics Forum · doi:10.1111/cgf.14012 · Cited by 23

A statistical shape modeling approach for predicting subject-specific human skull from head surface

Nguyen, T., Tran, V. D., Nguyen, H. Q., & Dao, T. T. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02219-4 · Cited by 23

Embedding 3D models in offline physical environments

Ertugrul, E., Zhang, H., Zhu, F., Lü, P., Li, P., Sheng, B., & Wu, E. (2020) · Computer Animation and Virtual Worlds · doi:10.1002/cav.1959 · Cited by 23

Enhanced facial expression recognition using 3D point sets and geometric deep learning

Nguyen, D.-P., Tho, M. H. B., & Dao, T. T. (2021) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-021-02383-1 · Cited by 23

Multimodal sentiment and emotion recognition in hyperbolic space

Araño, K. A., Orsenigo, C., Soto, M., & Vercellis, C. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115507 · Cited by 23

GeneCGAN: A conditional generative adversarial network based on genetic tree for point cloud reconstruction

Chen, C., Liu, D., Xu, C., & Truong, T. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.087 · Cited by 24

Geometric Deep Neural Network using Rigid and Non-Rigid Transformations for Human Action Recognition

Friji, R., Drira, H., Chaieb, F., Kchok, H., & Kurtek, S. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01238 · Cited by 23

Graph Neural Networks and Representation Embedding for Table Extraction in PDF Documents

Gemelli, A., Vivoli, E., & Marinai, S. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956590 · Cited by 28

A Network Science Perspective of Graph Convolutional Networks: A Survey

Jia, M., Gabryś, B., & Musiał, K. (2023) · IEEE Access · doi:10.1109/access.2023.3268797 · Cited by 24

Deep Learning-Driven Design of Robot Mechanisms

Purwar, A., & Chakraborty, N. (2023) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4062542 · Cited by 24

A Generalizable Indoor Propagation Model Based on Graph Neural Networks

Liu, S., Onishi, T., Taki, M., & Watanabe, S. (2023) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2023.3281061 · Cited by 23

Geometric learning and statistical modeling for surgical outcomes evaluation in craniosynostosis using 3D photogrammetry

Elkhill, C., Liu, J., Linguraru, M. G., LeBeau, S., Khechoyan, D. Y., French, B. M., & Porras, A. R. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107689 · Cited by 23

Long and Short-Range Dependency Graph Structure Learning Framework on Point Cloud

Liang, J., Du, Z., Liang, J., Yao, K., & Cao, F. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3298711 · Cited by 23

Multi-view enhanced zero-shot node classification

Wang, J., Wu, L., Zhao, H., & Jia, N. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103479 · Cited by 21

The Case for a Defect Genome Initiative

Yan, Q., Kar, S., Chowdhury, S., & Bansil, A. (2024) · Advanced Materials · doi:10.1002/adma.202303098 · Cited by 24

Why Topology for Machine Learning and Knowledge Extraction?

Ferri, M. (2018) · Machine Learning and Knowledge Extraction · doi:10.3390/make1010006 · Cited by 23

Integrative Analysis of Patient Health Records and Neuroimages via Memory-Based Graph Convolutional Network

Zhang, X., Chou, J., & Wang, F. (2018) · Proceedings · doi:10.1109/icdm.2018.00092 · Cited by 23

An Adaptive Multigrid Method Based on Path Cover

Hu, X., Lin, J., & Zikatanov, L. (2019) · SIAM Journal on Scientific Computing · doi:10.1137/18m1194493 · Cited by 23

Hybrid reasoning in knowledge graphs: Combing symbolic reasoning and statistical reasoning

Li, W., Qi, G., & Ji, Q. (2020) · Semantic Web · doi:10.3233/sw-190375 · Cited by 25

Inception neural network for complete intersection Calabi–Yau 3-folds

Erbin, H., & Finotello, R. (2021) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abda61 · Cited by 29

Knowledge embedded GCN for skeleton-based two-person interaction recognition

Li, J., Xie, X., Cao, Y., Pan, Q., Zhao, Z., & Shi, G. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.12.149 · Cited by 24

PeerNets: Exploiting Peer Wisdom Against Adversarial AttacksPreprint

Svoboda, J., Masci, J., Monti, F., Bronstein, M. M., & Guibas, L. J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.00088 · Cited by 21

Generating 3D faces using Convolutional Mesh AutoencodersPreprint

Ranjan, A., Bolkart, T., Sanyal, S., & Black, M. J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.10267 · Cited by 28

Graph Neural Based End-to-end Data Association Framework for Online Multiple-Object TrackingPreprint

Jiang, X., Li, P., Li, Y., & Zhen, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.05315 · Cited by 23

Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Protein Structures and Interaction NetworksPreprint

Jamasb, A. R., Viñas, R., Ma, E. J., Harris, C., Huang, K., Hall, D., Lió, P., & Blundell, T. L. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.15.204701 · Cited by 26

Decimated Framelet System on Graphs and Fast G-Framelet TransformsPreprint

Zheng, X., Zhong, B., Wang, Y. G., & Zhuang, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.06922 · Cited by 25

Interpretable Stability Bounds for Spectral Graph FiltersPreprint

Kenlay, H., Thanou, D., & Dong, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.09587 · Cited by 23

Coordinate Independent Convolutional Networks -- Isometry and Gauge Equivariant Convolutions on Riemannian ManifoldsPreprint

Weiler, M., Forré, P. D., Verlinde, E., & Welling, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06020 · Cited by 23

Reliable Soft Sensors With an Inherent Process Graph Constraint

Zhai, R., Zheng, J., Song, Z., & Ge, Z. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3372013 · Cited by 22

Learning weakly supervised audio-visual violence detection in hyperbolic space

Zhou, X. Z., Peng, X., Wen, H., Luo, Y., Yu, K., Yang, P., & Wu, Z. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105286 · Cited by 22

Integrating graph neural networks with physics-informed loss function for mechanical response prediction of hollow concrete structures with morphed honeycomb configurations

Wang, H., Tan, P. F. M., Foo, Y. Z., Owyong, S., Lu, Z.-R., Chen, H. T., Nguyen, T. H., & Lin, A. (2025) · Materials & Design · doi:10.1016/j.matdes.2025.113659 · Cited by 22

Deep Multi-structural Shape Analysis: Application to Neuroanatomy

Gutiérrez-Becker, B., & Wachinger, C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00931-1_60 · Cited by 21

Furnace thermal efficiency modeling using an improved convolution neural network based on parameter-adaptive mnemonic enhancement optimization

Wang, Y., Huang, J., Su, C., & Li, H. (2018) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2018.12.048 · Cited by 22

Deep Learning 3D Shapes Using Alt-az Anisotropic 2-Sphere Convolution

Liu, M., Yao, F., Choi, C., Sinha, A., & Karthik Ramani, A. (2018) · International Conference on Learning Representations · Cited by 23

Graphite: Graph-Induced Feature Extraction for Point Cloud Registration

Saleh, M., Dehghani, S., Busam, B., Navab, N., & Tombari, F. (2020) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv50981.2020.00034 · Cited by 24

Knowledge and Spatial Pyramid Distance-Based Gated Graph Attention Network for Remote Sensing Semantic Segmentation

Cui, W. H., He, X. J., Meng, Y., Wang, Z., Hao, Y., Li, J., Wu, W., Zhao, H., Xia, C., Li, J., et al. (2021) · Remote Sensing · doi:10.3390/rs13071312 · Cited by 22

On The Stability of Graph Convolutional Neural Networks Under Edge Rewiring

Kenlay, H., Thano, D., & Dong, X. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413474 · Cited by 22

View-Aware Geometry-Structure Joint Learning for Single-View 3D Shape Reconstruction

Zhang, X., Ma, R., Zou, C., Zhang, M., Zhao, X., & Gao, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3090917 · Cited by 22

Multi-Attention Based Spatial-Temporal Graph Convolution Networks for Traffic Flow Forecasting

Hu, J., & Chen, L. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534054 · Cited by 22

Transferability of graph neural networks: An extended graphon approach

Maskey, S., Levie, R., & Gitta Kutyniok, B. (2022) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2022.11.008 · Cited by 34

Table detection in business document images by message passing networks

Riba, P., Lutz Goldmann, A., Terrades, O. R., Rusticus, D., Fornés, A., & Lladós, J. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108641 · Cited by 22

Learning to Model the Relationship Between Brain Structural and Functional Connectomes

Li, Y., Mateos, G., & Zhang, Z. (2022) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2022.3209097 · Cited by 22

Publisher's Note:

Coleman, A., Eser, J., Mayotte, E. W., Sarazin, F., Schröder, F. G., Soldin, D., Venters, T. M., Aloisio, R., Alvarez-Muñiz, J., Batista, R. A., et al. (2022) · Astroparticle Physics · doi:10.1016/j.astropartphys.2022.102794 · Cited by 25

Representation learning of 3D meshes using an Autoencoder in the spectral domain

Lemeunier, C., Denis, F., Lavoué, G., & Florent Dupont, A. (2022) · Computers & Graphics · doi:10.1016/j.cag.2022.07.011 · Cited by 22

Three-Dimensional Reconstruction from a Single RGB Image Using Deep Learning: A Review

Khan, M. S. U., Pagani, A., Liwicki, M., Stricker, D., & Afzal, M. Z. (2022) · Journal of Imaging · doi:10.3390/jimaging8090225 · Cited by 22

Data-Driven Reliability Models of Quantum Circuit: From Traditional ML to Graph Neural Network

Saravanan, V., & Saeed, S. M. (2022) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2022.3202430 · Cited by 24

A Geometric Deep Learning Framework for Accurate Indoor Localization

Luo, X., & Meratnia, N. (2022) · International Conference on Indoor Positioning and Indoor Navigation · doi:10.1109/ipin54987.2022.9918118 · Cited by 24

FedRule: Federated Rule Recommendation System with Graph Neural Networks

Yao, Y., Kamani, M. M., Cheng, Z., Chen, L., Joe‐Wong, C., & Liu, T. (2023) · IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI) · doi:10.1145/3576842.3582328 · Cited by 22

Hyperspectral Image Classification Using Geometric Spatial–Spectral Feature Integration: A Class Incremental Learning Approach

Bai, J., Liu, R. M., Zhao, H., Xiao, Z. Q., Chen, Z., Shi, W., Xiong, Y., & Jiao, L. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3333005 · Cited by 22

From 3D point‐cloud data to explainable geometric deep learning: State‐of‐the‐art and future challenges

Saranti, A., Pfeifer, B., Gollob, C., Stampfer, K., & Holzinger, A. (2024) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1554 · Cited by 24

Experimental performance of graph neural networks on random instances of max-cut

Yao, W., Bandeira, A. S., & Villar, S. (2019) · Wavelets and Sparsity · doi:10.1117/12.2529608 · Cited by 22

Towards Shape-Based Knee Osteoarthritis Classification Using Graph Convolutional Networks

von Tycowicz, C. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098687 · Cited by 22

TARig: Adaptive template-aware neural rigging for humanoid characters

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