Geometric Deep Learning: Going beyond Euclidean data

Deep Residual Learning for Image Recognition

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Very Deep Convolutional Networks for Large-Scale Image Recognition

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ImageNet classification with deep convolutional neural networks

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Efficient Estimation of Word Representations in Vector Space

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Semi-supervised classification with graph convolutional networks

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Distributed Representations of Words and Phrases and their Compositionality

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Sequence to Sequence Learning with Neural Networks

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A Wavelet Tour of Signal Processing

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Predicting transmission loss in underwater acoustics using convolutional recurrent autoencoder network

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Differentially Private Graph Neural Networks for Whole-Graph Classification

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A survey of deep learning-based 3D shape generation

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HodgeNet: Graph Neural Networks for Edge Data

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Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures

Chauhan, J., Nathani, D., & Kaul, M. (2020) · Research Archive of Indian Institute of Technology Hyderabad (Indian Institute of Technology Hyderabad) · doi:10.48550/arxiv.2002.12815 · Cited by 45

MedmeshCNN - Enabling meshcnn for medical surface models

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Modeling Functional Similarity in Source Code With Graph-Based Siamese Networks

Mehrotra, N., Agarwal, N., Gupta, P., Anand, S., Lo, D., & Purandare, R. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2021.3105556 · Cited by 52

A geometry-informed deep learning framework for ultra-sparse 3D tomographic image reconstruction

Shen, L., Zhao, W., Capaldi, D. P. I., Pauly, J. M., & Xing, L. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105710 · Cited by 45

Robust learning from corrupted EEG with dynamic spatial filtering

Banville, H. J., Wood, S. U. N., Aimone, C., Engemann, D. A., & Gramfort, A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118994 · Cited by 45

PaSca: A Graph Neural Architecture Search System under the Scalable Paradigm

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MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property PredictionPreprint

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MeshGAN: Non-linear 3D Morphable Models of FacesPreprint

Cheng, S., Bronstein, M. M., Zhou, Y., Kotsia, I., Pantić, M., & Zafeiriou, S. P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.10384 · Cited by 66

Stability of Graph Scattering TransformsPreprint

Gama, F., Bruna, J., & Ribeiro, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.04784 · Cited by 47

GraphiT: Encoding Graph Structure in TransformersPreprint

Mialon, G., Chen, D., Selosse, M., & Mairal, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.05667 · Cited by 201

Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity

Venkatapathy, S., Votinov, M., Wagels, L., Kim, S., Lee, M., Habel, U., Ra, I.-H., & Jo, H. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1125339 · Cited by 46

Generalized Value Iteration Networks:Life Beyond Lattices

Niu, S., Chen, S., Guo, H., Targonski, C., Smith, M. C., & Kovačević, J. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12081 · Cited by 49

A geometric deep learning approach for checking element-to-entity mappings in infrastructure building information models

Koo, B., Jung, R.-K., Yu, Y., & Kim, I. (2020) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwaa075 · Cited by 44

Graph neural networks for single-cell omics data: a review of approaches and applications

Li, S., Hua, H., & Chen, S. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf109 · Cited by 43

A Neural Rendering Framework for Free-Viewpoint Relighting

Chen, Z., Chen, A., Zhang, G., Wang, C., Ji, Y., Kutulakos, K. N., & Jingyi Yu, A. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00564 · Cited by 43

Geometric deep learning on brain shape predicts sex and age

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Detecting interchanges in road networks using a graph convolutional network approach

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Graph neural network modelling as a potentially effective method for predicting and analyzing procedures based on patients' diagnoses

Ochoa, J. G. D., & Mustafa, F. E. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102359 · Cited by 44

BayeStab : Predicting effects of mutations on protein stability with uncertainty quantification

Wang, S., Tang, H., Zhao, Y., & Zuo, L. (2022) · Protein Science · doi:10.1002/pro.4467 · Cited by 43

Mangrove: Learning Galaxy Properties from Merger Trees

Jespersen, C. K., Cranmer, M., Melchior, P., Ho, S., Somerville, R. S., & Gabrielpillai, A. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac9b18 · Cited by 57

Machine learning prediction of side effects for drugs in clinical trials

Galeano, D., & Paccanaro, A. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100358 · Cited by 50

RadarGNN: Transformation Invariant Graph Neural Network for Radar-based Perception

Fent, F., Bauerschmidt, P., & Lienkamp, M. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00023 · Cited by 43

PastNet: Introducing Physical Inductive Biases for Spatio-temporal Video Prediction

Wu, H., Xu, F., Chen, C., Hua, X., Luo, X., & Wang, H. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681489 · Cited by 43

Representation Learning on Graphs

M., K. R. P., Mohan, A., & Srinivasa, K. G. (2018) · Computer communications and networks · doi:10.1007/978-3-319-96746-2_15 · Cited by 47

Scalable Graph Convolutional Networks With Fast Localized Spectral Filter for Directed Graphs

Li, C., Qin, X., Xu, X., Yang, D., & Wei, G. (2020) · IEEE Access · doi:10.1109/access.2020.2999520 · Cited by 47

Learning Part Boundaries from 3D Point Clouds

Loizou, M., Averkiou, M., & Kalogerakis, E. (2020) · Computer Graphics Forum · doi:10.1111/cgf.14078 · Cited by 42

Mapping Land Use from High Resolution Satellite Images by Exploiting the Spatial Arrangement of Land Cover Objects

Li, M., & Stein, A. (2020) · Remote Sensing · doi:10.3390/rs12244158 · Cited by 43

Learning Dynamical Human-Joint Affinity for 3D Pose Estimation in Videos

Zhang, J., Wang, Y., Zhou, Z., Luan, T., Wang, Z., & Qiao, Y. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3109517 · Cited by 43

Graph Element Networks: adaptive, structured computation and memoryPreprint

Alet, F., Jeewajee, A. K., Bauzá, M., Rodríguez, A., Lozano‐Pérez, T., & Kaelbling, L. P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.09019 · Cited by 47

A Short Tutorial on The Weisfeiler-Lehman Test And Its Variants

Huang, N. T., & Villar, S. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413523 · Cited by 55

A novel graph neural network method for Alzheimer’s disease classification

Zhou, Z., Wang, Q., An, X., Chen, S., Sun, Y., Wang, G., & Yan, G. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108869 · Cited by 45

A deep community based approach for large scale content based X-ray image retrieval

Haq, N. F., Moradi, M., & Wang, Z. J. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101847 · Cited by 44

Real-time liver tumor localization via a single x-ray projection using deep graph neural network-assisted biomechanical modeling

Shao, H., Wang, J., Bai, T., Chun, J., Park, J. C., Jiang, S. B., & Zhang, Y. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac6b7b · Cited by 42

Transferable graph features-driven cross-domain rotating machinery fault diagnosis

Yang, C., Liu, J., Zhou, K., Ge, M., & Jiang, X. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109069 · Cited by 44

Learning Resilient Radio Resource Management Policies With Graph Neural Networks

Naderializadeh, N., Eisen, M., & Ribeiro, A. (2023) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2023.3255547 · Cited by 42

Pixel2Mesh++: 3D Mesh Generation and Refinement From Multi-View Images

Wen, C., Zhang, Y., Cao, C., Li, Z., Xue, X., & Fu, Y. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3169735 · Cited by 43

CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing

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Graph Neural Network-based Android Malware Classification with Jumping Knowledge

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Materials informatics: A review of AI and machine learning tools, platforms, data repositories, and applications to architectured porous materials

Živić, F., Kaplarevic-Malisic, A. M., Grujović, N., Stojanovic, B. S., & Ivanovic, M. R. (2025) · Materials Today Communications · doi:10.1016/j.mtcomm.2025.113525 · Cited by 51

Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds

Grattarola, D., Zambon, D., Livi, L., & Alippi, C. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2927301 · Cited by 51

Learning Graph Distances with Message Passing Neural Networks

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Duo: Differential Fuzzing for Deep Learning Operators

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Bayesian Semi-supervised Learning with Graph Gaussian ProcessesPreprint

Ng, Y. C., Colombo, N., & Silva, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.04379 · Cited by 41

Bridging the Gap Between Spectral and Spatial Domains in Graph Neural NetworksPreprint

Balcılar, M., Renton, G., Héroux, P., Gaüzère, B., Adam, S., & Honeiné, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11702 · Cited by 74

Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning

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Neural fields for rapid aircraft aerodynamics simulations

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A Recurrent Graph Neural Network for Multi-relational Data

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RealPoint3D: An Efficient Generation Network for 3D Object Reconstruction From a Single Image

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Path integral based convolution and pooling for graph neural networks*

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VC-Net: Deep Volume-Composition Networks for Segmentation and Visualization of Highly Sparse and Noisy Image Data

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Predicting Biomedical Interactions With Higher-Order Graph Convolutional Networks

Kishan, K. C., Li, R., Cui, F., & Haake, A. R. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3059415 · Cited by 46

Neural software vulnerability analysis using rich intermediate graph representations of programs

Ghaffarian, S. M., & Shahriari, H. R. (2020) · Information Sciences · doi:10.1016/j.ins.2020.11.053 · Cited by 42

A Cascade Graph Convolutional Network for Predicting Protein–Ligand Binding Affinity

Shen, H., Zhang, Y., Zheng, C.-H., Wang, B., & Chen, P. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22084023 · Cited by 41

Heterogeneous Spatio-Temporal Graph Convolution Network for Traffic Forecasting with Missing Values

Zhong, W., Suo, Q., Jia, X., Zhang, A., & Su, L. (2021) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs51616.2021.00073 · Cited by 43

CenGCN: Centralized Convolutional Networks with Vertex Imbalance for Scale-Free Graphs

Xia, F., Wang, L., Tang, T., Chen, X., Kong, X., Oatley, G., & King, I. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3149888 · Cited by 41

Mixed-curvature Variational AutoencodersPreprint

Skopek, O., Ganea, O.-E., & Bécigneul, G. (2019) · arXiv (Cornell University) · doi:10.3929/ethz-b-000372387 · Cited by 72

Artificial intelligence approaches to human-microbiome protein–protein interactions

Lim, H., Cankara, F., Tsai, C.-J., Keskin, O., Nussinov, R., & Gursoy, A. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102328 · 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

Advances in the application of stereo vision in aquaculture with emphasis on fish: A review

Li, D., Yu, J., Du, Z., Xu, W., Wang, G., Zhao, S., Liu, Y., & Akhter, M. (2024) · Reviews in Aquaculture · doi:10.1111/raq.12919 · Cited by 40

RCNet: Dual-Network Resonance Collaboration via Mutual Learning for RGB-D Road Defect Detection

Zhou, W., Ju, Z., Cong, R., & Yan, W. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3617769 · Cited by 39

Learning 3D Keypoint Descriptors for Non-rigid Shape Matching

Wang, H., Guo, J., Dong-ming Yan, A., Quan, W., & Zhang, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01237-3_1 · 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

Novel Arithmetics in Deep Neural Networks Signal Processing for Autonomous Driving: Challenges and Opportunities

Cococcioni, M., Rossi, F., Ruffaldi, E., Saponara, S., & de Dinechin, B. D. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2988436 · Cited by 42

Continuous conditional random field convolution for point cloud segmentation

Yang, F., Davoine, F., Wang, H., & Jin, Z. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108357 · Cited by 39

A Representation Learning Framework for Property Graphs

Hou, Y., Chen, H., Li, C., Cheng, J. S.-C., & Yang, M.-C. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330948 · Cited by 38

LBS Autoencoder: Self-Supervised Fitting of Articulated Meshes to Point Clouds

Li, C., Simon, T., Saragih, J., Póczos, B., & Sheikh, Y. A. (2019) · 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/cvpr.2019.01224 · Cited by 37

CurvaNet: Geometric Deep Learning based on Directional Curvature for 3D Shape Analysis

He, W., Jiang, Z., Zhang, C., & Sainju, A. M. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403272 · Cited by 38

How to teach neural networks to mesh: Application on 2-D simplicial contours

Papagiannopoulos, A., Clausen, P., & Avellan, F. (2021) · Neural Networks · doi:10.1016/j.neunet.2020.12.019 · Cited by 41

Graph-based deep learning frameworks for molecules and solid-state materials

Gong, W., & Yan, Q. (2021) · Computational Materials Science · doi:10.1016/j.commatsci.2021.110332 · Cited by 40

Graph coarsening: from scientific computing to machine learning

Chen, J., Saad, Y., & Zhang, Z. (2022) · SeMA Journal · doi:10.1007/s40324-021-00282-x · Cited by 38

Predicting Human Mobility via Graph Convolutional Dual-attentive Networks

Dang, W., Wang, H., Pan, S., Zhang, P., Zhou, C. D., Chen, X., & Wang, J. (2022) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3488560.3498400 · 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

Dynamically Pruned Message Passing Networks for Large-Scale Knowledge Graph ReasoningPreprint

Xu, X., Feng, W., Jiang, Y., Xie, X., Sun, Z., & Deng, Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.11334 · Cited by 39

Recognizing fish behavior in aquaculture with graph convolutional network

Huang, J., Yu, X., Chen, X., An, D., Zhou, Y., & Wei, Y. (2022) · Aquacultural Engineering · doi:10.1016/j.aquaeng.2022.102246 · Cited by 37

Urban regional function guided traffic flow prediction

Wang, K., Liu, L., Liu, Y., Li, G., Zhou, F., & Lin, L. (2023) · Information Sciences · doi:10.1016/j.ins.2023.03.109 · 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

Real-Time Seismic Intensity Prediction Using Self-Supervised Contrastive GNN for Earthquake Early Warning

Murshed, R. U., Noshin, K., Zakaria, M. A., Uddin, M. F., Amin, A. F. M. S., & Ali, M. E. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3373643 · Cited by 37

Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex Networks

Villalba-Díez, J., Molina, M., Ordieres-Meré, J. B., Sun, S., Schmidt, D., & Wellbrock, W. (2020) · Sensors · doi:10.3390/s20030763 · Cited by 37

Synthesizing Facial Photometries and Corresponding Geometries Using Generative Adversarial Networks

Shamai, G., Slossberg, R., & Kimmel, R. (2019) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3337067 · Cited by 37

Mesh Variational Autoencoders with Edge Contraction Pooling

Yuan, Y.-J., Lai, Y., Yang, J., Duan, Q., Fu, H., & Gao, L. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00145 · Cited by 37

Predicting Intentions of Pedestrians from 2D Skeletal Pose Sequences with a Representation-Focused Multi-Branch Deep Learning Network

Gesnouin, J., Pechberti, S., Bresson, G., Stanciulescu, B., & Moutarde, F. (2020) · Algorithms · doi:10.3390/a13120331 · Cited by 41

Introduction to Graph Neural Networks

Liu, Z., & Zhou, J. (2020) · Synthesis lectures on artificial intelligence and machine learning · doi:10.2200/s00980ed1v01y202001aim045 · Cited by 37

Restricted Delaunay Triangulation for Explicit Surface Reconstruction

Wang, P., Wang, Z., Xin, S., Gao, X., Wang, W., & Tu, C. (2022) · ACM Transactions on Graphics · doi:10.1145/3533768 · Cited by 37

Cell Complex Neural NetworksPreprint

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

Multi-view Consensus CNN for 3D Facial Landmark Placement

Paulsen, R. R., Juhl, K. A., Haspang, T. M., Hansen, T., Ganz, M., & Einarsson, G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20887-5_44 · Cited by 36

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

AggMapNet: enhanced and explainable low-sample omics deep learning with feature-aggregated multi-channel networks

Shen, W. X., Liu, Y., Chen, Y., Zeng, X., Tan, Y., Jiang, Y. Y., & Chen, Y. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac010 · Cited by 34

A discriminative SPD feature learning approach on Riemannian manifolds for EEG classification

Kim, B. H., Choi, J. W., Lee, H., & Jo, S. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109751 · Cited by 36

Beyond Grid Data: Exploring graph neural networks for Earth observation

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