Geometric Deep Learning: Going beyond Euclidean data

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ImageNet classification with deep convolutional neural networks

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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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deepAMPNet: a novel antimicrobial peptide predictor employing AlphaFold2 predicted structures and a bi-directional long short-term memory protein language model

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Adversarial autoencoders with constant-curvature latent manifolds

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Hexagonal Convolutional Neural Networks for Hexagonal Grids

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Machine learning for complete intersection Calabi-Yau manifolds: A methodological study

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Fractional Spectral Graph Wavelets and Their Applications

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Parameterization for polynomial curve approximation via residual deep neural networks

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SGAE: Stacked Graph Autoencoder for Deep Clustering

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GraphSite: Ligand Binding Site Classification with Deep Graph Learning

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3D visualization processes for recreating and studying organismal form

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RepPVConv: attentively fusing reparameterized voxel features for efficient 3D point cloud perception

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DeepStruc: towards structure solution from pair distribution function data using deep generative models

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Spectral coarsening of geometric operators

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Clique pooling for graph classificationPreprint

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A comprehensive review of convolutional neural networks: foundations, enhancements and applications

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Parametric design for human body modeling by wireframe-assisted deep learning

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Graphon Filters: Graph Signal Processing in the Limit

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Edge-based sequential graph generation with recurrent neural networks

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Learnable Pooling in Graph Convolutional Networks for Brain Surface Analysis

Gopinath, K. K., Desrosiers, C., & Lombaert, H. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3028391 · Cited by 31

Learning Structural Similarity of User Interface Layouts Using Graph Networks

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Continuous Geodesic Convolutions for Learning on 3D Shapes

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GeoConv: Geodesic guided convolution for facial action unit recognition

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Augmented Graph Neural Network with hierarchical global-based residual connections

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IFC BIM Model Enrichment with Space Function Information Using Graph Neural Networks

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PSNet: Fast Data Structuring for Hierarchical Deep Learning on Point Cloud

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A multi-information fusion model for short term load forecasting of an architectural complex considering spatio-temporal characteristics

Xie, J., Zhong, Y., Xiao, T., Wang, Z., Zhang, J., Wang, T., & Schuller, B. W. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.112566 · Cited by 28

Adversarial Diffusion Attacks on Graph-Based Traffic Prediction Models

Zhu, L., Feng, K., Pu, Z., & Ma, W. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3290401 · Cited by 32

Transferability Properties of Graph Neural Networks

Ruiz, L., Chamon, L. F. O., & Ribeiro, A. (2023) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2023.3297848 · Cited by 28

SurReal: Complex-Valued Learning as Principled Transformations on a Scaling and Rotation Manifold

Chakraborty, R., Xing, Y., & Yu, S. X. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3030565 · Cited by 32

Unlocking the Potential: Predicting Redox Behavior of Organic Molecules, from Linear Fits to Neural Networks

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Semantic-aware quality assessment of building elements using graph neural networks

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UCoDe: unified community detection with graph convolutional networks

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Machine learning in high energy physics: a review of heavy-flavor jet tagging at the LHC

Mondal, S., & Mastrolorenzo, L. (2024) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-024-01234-y · Cited by 42

Machine Learning and Artificial Intelligence in Drug Repurposing—Challenges and Perspectives

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Opportunities and challenges of graph neural networks in electrical engineering

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Persistent Topological Laplacians—A Survey

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Comparison of Deep Neural Networks and Deep Hierarchical Models for Spatio-Temporal Data

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Learned 3D Shape Representations Using Fused Geometrically Augmented Images: Application to Facial Expression and Action Unit Detection

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Pixel2point: 3D Object Reconstruction From a Single Image Using CNN and Initial Sphere

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Self-Constructing Graph Convolutional Networks for Semantic Labeling

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Semi-supervised two-phase familial analysis of Android malware with normalized graph embedding

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Applications of Physics-Informed Scientific Machine Learning in Subsurface Science: A Survey

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Graph-Based Visual-Semantic Entanglement Network for Zero-Shot Image Recognition

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Machine learning enabled identification of potential SARS-CoV-2 3CLpro inhibitors based on fixed molecular fingerprints and Graph-CNN neural representations

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Dual Mutual Robust Graph Convolutional Network for Weakly Supervised Node Classification in Social Networks of Internet of People

Li, B., Wu, J. X., Pi, D., & Lin, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3091883 · Cited by 27

Detecting Fake News on Social Media

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Geometric Multimodal Deep Learning With Multiscaled Graph Wavelet Convolutional Network

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State Estimation in Electric Power Systems Leveraging Graph Neural Networks

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Graph Signal Sampling Under Stochastic Priors

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Efficient classical algorithms for simulating symmetric quantum systems

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Fast Approximation of Laplace‐Beltrami Eigenproblems

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Learning Symmetries of Classical Integrable SystemsPreprint

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Image Classification with Hierarchical Multigraph NetworksPreprint

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A Non-Negative Factorization approach to node pooling in Graph Convolutional Neural Networks.

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GraphCL: Contrastive Self-Supervised Learning of Graph RepresentationsPreprint

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Relating Graph Neural Networks to Structural Causal ModelsPreprint

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Surfel convolutional neural network for support detection in additive manufacturing

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A data augmentation scheme for geometric deep learning in personalized brain–computer interfaces.

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Epilepsy Classification for Mining Deeper Relationships between EEG Channels based on GCN

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Metabolomics in the era of artificial intelligence

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Gegenbauer Graph Neural Networks for Time-Varying Signal Reconstruction

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A review on deep anomaly detection in blockchain

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Transferable and data efficient metamodeling of storm water system nodal depths using auto-regressive graph neural networks

Garzón, A., Kapelan, Z. S., Langeveld, J., & Taormina, R. (2024) · Water Research · doi:10.1016/j.watres.2024.122396 · Cited by 29

A Survey of State of the Art Methods Employed in the Offline Signature Verification Process

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360° Depth Estimation from Multiple Fisheye Images with Origami Crown Representation of Icosahedron

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Discriminative and generative models for anatomical shape analysis on point clouds with deep neural networks

Gutiérrez-Becker, B., Sarasúa, I., & Wachinger, C. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101852 · Cited by 26

Graph Convolutional Networks for Learning with Few Clean and Many Noisy Labels

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ADVANCING DEEP LEARNING FOR EARTH SCIENCES: FROM HYBRID MODELING TO INTERPRETABILITY

Camps‐Valls, G., Reichstein, M., Zhu, X. X., & Tuia, D. (2020) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss39084.2020.9323558 · Cited by 26

Multi-view spectral graph convolution with consistent edge attention for molecular modeling

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MGN-Net: A multi-view graph normalizer for integrating heterogeneous biological network populations

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Equivariant Wavelets: Fast Rotation and Translation Invariant Wavelet Scattering Transforms

Saydjari, A. K., & Finkbeiner, D. P. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3165730 · Cited by 29

Graph Neural Networks for Traffic Forecasting

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A voxelized point clouds representation for object classification and segmentation on 3D data

Gezawa, A. S., Bello, Z. A., Wang, Q., & Yunqi, L. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-021-03899-x · Cited by 27

Disentangling Multi-Facet Social Relations for Recommendation

Sha, X., Sun, Z., & Zhang, J. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3108794 · Cited by 26

Predicting the retinotopic organization of human visual cortex from anatomy using geometric deep learning

Ribeiro, F. L., Bollmann, S., & Puckett, A. M. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118624 · Cited by 27

MFAGCN: A new framework for identifying power grid branch parameters

Xia, M., Wang, Z., Lu, M., & Pan, L. (2022) · Electric Power Systems Research · doi:10.1016/j.epsr.2022.107855 · Cited by 26

Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution

Zhuang, Z., Si, L., Wang, S., Xuan, K., Ouyang, X., Zhan, Y., Xue, Z., Zhang, L., Shen, D. G., Yao, W., et al. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3206042 · Cited by 29

Automatic Seizure Identification from EEG Signals Based on Brain Connectivity Learning

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Weakly supervised inference of personalized heart meshes based on echocardiography videos

Laumer, F., Amrani, M., Manduchi, L., Beuret, A., Rubi, L., Dubatovka, A., Matter, C. M., & Buhmann, J. M. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102653 · Cited by 26

Learning graph deep autoencoder for anomaly detection in multi-attributed networks

Shao, M., Lin, Y., Peng, Q., Zhao, J., Pei, Z., & Sun, Y. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110084 · Cited by 25

3D Human Pose Estimation Using Möbius Graph Convolutional Networks

Azizi, N., Possegger, H., Rodolà, E., & Bischof, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19769-7_10 · Cited by 26

A deep-learning method for the end-to-end prediction of intracranial aneurysm rupture risk

Li, P., Liu, Y., Zhou, J., Tu, S., Zhao, B., Wan, J., Yang, Y., & Xu, L. (2023) · Patterns · doi:10.1016/j.patter.2023.100709 · Cited by 27

Toward fair graph neural networks via real counterfactual samples

Wang, Z., Qiu, M., Chen, M., Salem, M. B., Yao, X., & Zhang, W. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02161-z · Cited by 28

Deep Cascade Generation on Point Sets

Wang, K., Chen, K., & Jia, K. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/517 · Cited by 26

Just Jump: Dynamic Neighborhood Aggregation in Graph Neural NetworksPreprint

Fey, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.04849 · Cited by 41

Single Image 3D Hand Reconstruction with Mesh ConvolutionsPreprint

Kulon, D., Wang, H., Güler, R. A., Bronstein, M. M., & Zafeiriou, S. P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.01326 · Cited by 26

Neural Execution of Graph AlgorithmsPreprint

Veličković, P., Ying, R., Padovano, M., Hadsell, R. T., & Blundell, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.10593 · Cited by 78

Finding Patient Zero: Learning Contagion Source with Graph Neural NetworksPreprint

Shah, C., Dehmamy, N., Perra, N., Chinazzi, M., Barabási, A.-Ĺ., Vespignani, A., & Yu, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.11913 · Cited by 26

EdgeNets: Edge Varying Graph Neural Networks

Isufi, E., Gama, F., & Ribeiro, A. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3111054 · Cited by 102

A computationally efficient hybrid neural network architecture for porous media: Integrating convolutional and graph neural networks for improved property predictions

Zhao, Q., Han, X., Guo, R., & Chen, C. (2024) · Advances in Water Resources · doi:10.1016/j.advwatres.2024.104881 · Cited by 28

Facing & mitigating common challenges when working with real-world data: The Data Learning Paradigm

Lever, J., Cheng, S. L., Quilodrán-Casas, C., Liu, C., Fan, H., Platt, R. W., Rakotoharisoa, A., Johnson, E., Li, S., Shang, Z., et al. (2025) · Journal of Computational Science · doi:10.1016/j.jocs.2024.102523 · Cited by 29

Improving Neural Relation Extraction with Implicit Mutual Relations

Kuang, J., Cao, Y., Zheng, J., He, X., Gao, M., & Zhou, A. (2020) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde48307.2020.00093 · Cited by 24

On the application of neural networks for temperature field measurements using thermochromic liquid crystals

Möller, S., Resagk, C., & Cierpka, C. (2020) · Experiments in Fluids · doi:10.1007/s00348-020-2943-7 · Cited by 25

Digraph Signal Processing With Generalized Boundary Conditions

Seifert, B., & Püschel, M. (2021) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2021.3051267 · Cited by 25

Learning on 3D Meshes With Laplacian Encoding and Pooling

Qiao, Y.-L., Gao, L., Yang, J., Rosin, P. L., Lai, Y., & Chen, X. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3014449 · Cited by 35

Hypergraph wavelet neural networks for 3D object classification

Nong, L., Wang, J., Lin, J., Qiu, H., Zheng, L., & Zhang, W. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.006 · Cited by 26

Holistic Graph Neural Networks based on a global-based attention mechanism

Rassil, A., Chougrad, H., & Zouaki, H. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.108105 · Cited by 25

Kernel Regression Over Graphs Using Random Fourier Features

Elias, V. R. M., Gogineni, V. C., Martins, W. A., & Werner, S. (2022) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2022.3149134 · Cited by 25

Estimating the state of epidemics spreading with graph neural networks

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