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

Semi-supervised learning with graph convolutional extreme learning machines

Zhang, Z., Cai, Y., & Gong, W. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119164 · Cited by 36

Spatio-Temporal Graph Convolutional Networks via View Fusion for Trajectory Data Analytics

Hu, W., Li, W., Zhou, X., Kawai, A., Fueda, K., Qian, Q., & Wang, J. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3210559 · Cited by 36

Geometric deep learning on graphs and manifolds using mixture model CNNsPreprint

Monti, F., Boscaini, D., Masci, J., Rodolà, E., Svoboda, J., & Bronstein, M. M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.08402 · Cited by 87

Low-data interpretable deep learning prediction of antibody viscosity using a biophysically meaningful representation

Rai, B. K., Apgar, J. R., & Bennett, E. M. (2023) · Scientific Reports · doi:10.1038/s41598-023-28841-4 · Cited by 34

State of the Art and Potentialities of Graph-level Learning

Yang, Z., Zhang, G., Wu, J. X., Yang, J. Y., Sheng, Q. Z., Xue, S., Zhou, C. D., Aggarwal, C. C., Peng, H., Hu, W., et al. (2024) · ACM Computing Surveys · doi:10.1145/3695863 · Cited by 44

A Novel Interpretable Graph Convolutional Neural Network for Multimodal Brain Tumor Segmentation

Choudhry, I. A., Iqbal, S., Alhussein, M. A., Aurangzeb, K., Qureshi, A. N., & Hussain, A. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10387-w · Cited by 36

Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources

Willard, J., Varadharajan, C., Jia, X., & Kumar, V. (2025) · Environmental Data Science · doi:10.1017/eds.2024.14 · Cited by 49

Joint embedding of structure and features via graph convolutional networks

Lerique, S., Abitbol, J. L., & Karsai, M. (2020) · Applied Network Science · doi:10.1007/s41109-019-0237-x · Cited by 37

What Does It Mean to Learn in Deep Networks? And, How Does One Detect Adversarial Attacks?

Corneanu, C. A., Madadi, M., Escalera, S., & Martı́nez, A. M. (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.2019.00489 · Cited by 38

Learning Localized Representations of Point Clouds With Graph-Convolutional Generative Adversarial Networks

Valsesia, D., Fracastoro, G., & Magli, E. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.2976627 · Cited by 35

Deep Regression on Manifolds: A 3D Rotation Case Study

Brégier, R. (2021) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv53792.2021.00027 · Cited by 35

DSG-Net: Learning Disentangled Structure and Geometry for 3D Shape Generation

Yang, J., Mo, K., Lai, Y., Guibas, L. J., & Gao, L. (2022) · ACM Transactions on Graphics · doi:10.1145/3526212 · Cited by 35

Spatiotemporal multi-graph convolutional networks with synthetic data for traffic volume forecasting

Zhu, K. N., Zhang, S., Li, J., Zhou, D., Dai, H., & Hu, Z. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115992 · Cited by 35

TM-NET

Gao, L., Wu, T., Yuan, Y., Lin, M.-X., Lai, Y., & Zhang, H. (2021) · ACM Transactions on Graphics · doi:10.1145/3478513.3480503 · Cited by 35

Seizure localisation with attention-based graph neural networks

Grattarola, D., Livi, L., Alippi, C., Wennberg, R. A., & Valiante, T. A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117330 · Cited by 36

Graph Neural Networks: A bibliometrics overview

Keramatfar, A., Rafiee, M., & Amirkhani, H. (2022) · Machine Learning with Applications · doi:10.1016/j.mlwa.2022.100401 · Cited by 39

Deep learning-based semantic segmentation of urban-scale 3D meshes in remote sensing: A survey

Adam, J. M., Liu, W., Zang, Y., Afzal, M. K., Bello, S. A., Muhammad, A. U., Wang, C., & Li, J. (2023) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103365 · Cited by 35

GMLS-Nets: A Framework for Learning from Unstructured Data

Sandia National Laboratories (SNL), A., Trask, N., Patel, R., Gross, B., & Atzberger, P. J. (2019) · doi:10.2172/1763256 · Cited by 35

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

Estimating the state of epidemics spreading with graph neural networks

Tomy, A., Razzanelli, M., Lauro, F. D., Rus, D. L., & Santina, C. D. (2022) · Nonlinear Dynamics · doi:10.1007/s11071-021-07160-1 · Cited by 37

TaG-Net: Topology-Aware Graph Network for Centerline-Based Vessel Labeling

Yao, L., Shi, F., Wang, S., Zhang, X., Xue, Z., Cao, X., Zhan, Y., Chen, L., Chen, Y., Song, B., et al. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3240825 · Cited by 35

Graph-ensemble fusion for enhanced IoT intrusion detection: leveraging GCN and deep learning

Mittal, K., & Batra, P. K. (2024) · Cluster Computing · doi:10.1007/s10586-024-04404-8 · Cited by 34

Dilated Convolutional Neural Networks for Sequential Manifold-Valued Data

Chakraborty, R., Zhen, X., Vogt, N., Bendlin, B. B., & Singh, V. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01072 · Cited by 35

Parameterized Hypercomplex Graph Neural Networks for Graph Classification

Le, T., Bertolini, M., Noé, F., & Clevert, D.-A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86365-4_17 · Cited by 34

H-CNN: Spatial Hashing Based CNN for 3D Shape Analysis

Shao, T., Yang, Y., Weng, Y., Hou, Q., & Zhou, K. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2887262 · Cited by 35

Robust Graph Neural-Network-Based Encoder for Node and Edge Deep Anomaly Detection on Attributed Networks

Daniel, G. V., Chandrasekaran, K., Venkatesan, M., & Panneer, P. (2023) · Electronics · doi:10.3390/electronics12061501 · Cited by 33

HGWaveNet: A Hyperbolic Graph Neural Network for Temporal Link Prediction

Bai, Q., Nie, C., Zhang, H., Zhao, D., & Yuan, X. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583455 · Cited by 33

Semantic Point Cloud Segmentation with Deep-Learning-Based Approaches for the Construction Industry: A Survey

Rauch, L., & Braml, T. (2023) · Applied Sciences · doi:10.3390/app13169146 · Cited by 36

Credit risk prediction for small and medium enterprises utilizing adjacent enterprise data and a relational graph attention network

Wang, J., Liu, G., Xu, X., & Xing, X. (2024) · Journal of Management Science and Engineering · doi:10.1016/j.jmse.2023.11.005 · Cited by 33

Graph deep learning recognition of port ship behavior patterns from a network approach

Xin, R., Pan, J., Yang, F., Yan, X., Ai, B., & Zhang, Q. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.117921 · Cited by 33

Depth-based subgraph convolutional auto-encoder for network representation learning

Zhang, Z., Chen, D., Wang, Z., Li, H., Bai, L., & Hancock, E. R. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.01.045 · Cited by 33

Open Questions about Generative Adversarial Networks

Odena, A. (2019) · Distill · doi:10.23915/distill.00018 · Cited by 37

Point Cloud Super Resolution with Adversarial Residual Graph Networks

Wu, H., & Huang, K. (2020) · British Machine Vision Conference (BMVC) · doi:10.5244/c.34.32 · Cited by 33

MGCN

Wang, Y., Ren, J., Dong-ming Yan, A., Guo, J., Zhang, X., & Wonka, P. (2020) · ACM Transactions on Graphics · doi:10.1145/3386569.3392443 · Cited by 33

Data Integration Using Advances in Machine Learning in Drug Discovery and Molecular Biology

Hudson, I. L. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0826-5_7 · Cited by 36

Propagation source identification of infectious diseases with graph convolutional networks

Li, L., Zhou, J., Jiang, Y., & Huang, B. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103720 · Cited by 36

Multi-Dimensional Edge Features Graph Neural Network on Few-Shot Image Classification

Xiong, C., Wen, L., Liu, Y., & Wang, M. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3061978 · Cited by 33

LayoutGMN: Neural Graph Matching for Structural Layout Similarity

Patil, A. G., Li, M., Fisher, M., Savva, M., & Zhang, H. (2021) · 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/cvpr46437.2021.01090 · Cited by 33

Deep Learning-Based Automated Extraction of Anthropometric Measurements From a Single 3-D Scan

Kaashki, N. N., Hu, P., & Munteanu, A. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3106126 · Cited by 33

Predicting age and clinical risk from the neonatal connectome

Taoudi-Benchekroun, Y., Christiaens, D., Grigorescu, I., Gale‐Grant, O., Schuh, A., Pietsch, M., Chew, A. T. M., Harper, N., Falconer, S., Poppe, T., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119319 · Cited by 35

Structure-Based Protein Function Prediction using Graph Convolutional NetworksPreprint

Gligorijević, V., Renfrew, P. D., Kościółek, T., Leman, J. K., Berenberg, D., Vatanen, T., Chandler, C., Taylor, B. C., Fisk, I. M., Vlamakis, H. C., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/786236 · Cited by 33

How does Heterophily Impact the Robustness of Graph Neural Networks?

Zhu, J., Jin, J., Loveland, D., Schaub, M. T., & Koutra, D. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539418 · Cited by 34

Unsupervised ensemble-based phenotyping enhances discoverability of genes related to left-ventricular morphology

Bonazzola, R., Ferrante, E., Ravikumar, N., Xia, Y., Keavney, B. D., Plein, S., Syeda-Mahmood, T. F., & Frangi, A. F. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00801-1 · Cited by 31

Conv-RGNN: An efficient Convolutional Residual Graph Neural Network for ECG classification

Qiang, Y., Dong, X., Liu, X., Yang, Y., Fang, Y., & Dou, J. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108406 · Cited by 36

A Novel Hybrid Model Detection of Security Vulnerabilities in Industrial Control Systems and IoT Using GCN+LSTM

Koca, M., & Avcı, İ. (2024) · IEEE Access · doi:10.1109/access.2024.3466391 · Cited by 32

Tensor Graph Convolutional Networks for Multi-Relational and Robust Learning

Ioannidis, V. N., Marques, A. G., & Giannakis, G. B. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.3028495 · Cited by 33

Hierarchical graph attention networks for semi-supervised node classification

Li, K., Feng, Y., Gao, Y., & Qiu, J. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01729-w · Cited by 34

Learning to Plan with Uncertain Topological Maps

Beeching, E., Dibangoye, J., Simonin, O., & Wolf, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58580-8_28 · Cited by 38

Visual Analytics for Temporal Hypergraph Model Exploration

Fischer, M. T., Arya, D., Streeb, D., Seebacher, D., Keim, D. A., & Worring, M. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3030408 · Cited by 37

Learning graph edit distance by graph neural networks

Riba, P., Fischer, A., Lladós, J., & Fornés, A. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108132 · Cited by 38

Toward understanding COVID-19 pneumonia: a deep-learning-based approach for severity analysis and monitoring the disease

Zandehshahvar, M., van Assen, M., Maleki, H., Kiarashi, Y., Cecco, C. N. D., & Adibi, A. (2021) · Scientific Reports · doi:10.1038/s41598-021-90411-3 · Cited by 30

Energy-weighted message passing: an infra-red and collinear safe graph neural network algorithm

Konar, P., Ngairangbam, V. S., & Spannowsky, M. (2021) · Journal of High Energy Physics · doi:10.1007/jhep02(2022)060 · Cited by 32

Interaction data are identifiable even across long periods of time

Creţu, A.-M., Monti, F., Marrone, S., Dong, X., Bronstein, M. M., & de Montjoye, Y.-A. (2022) · Nature Communications · doi:10.1038/s41467-021-27714-6 · Cited by 32

Towards learning trustworthily, automatically, and with guarantees on graphs: An overview

Oneto, L., Navarin, N., Biggio, B., Errica, F., Micheli, A., Scarselli, F., Bianchini, M., Demetrio, L., Bongini, P., Tacchella, A., et al. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.072 · Cited by 34

Interpretable cardiac anatomy modeling using variational mesh autoencoders

Beetz, M., Acero, J. C., Banerjee, A., Eitel, I., Zacur, E., Lange, T., Stiermaier, T., Evertz, R., Backhaus, S. J., Thiele, H., et al. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.983868 · Cited by 36

A graph neural network based deep reinforcement learning algorithm for multi-agent leader-follower flocking

Xiao, J. Q., Wang, Z., He, J., & Yuan, G. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119074 · Cited by 31

Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks

Hong, Y., Kim, J., Chen, G., Lin, W., Yap, P., & Shen, D. G. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2911203 · Cited by 32

Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine LearningPreprint

Addanki, R., Venkatakrishnan, S. B., Gupta, S., Mao, H., & Alizadeh, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.08879 · Cited by 32

FutureMapping 2: Gaussian Belief Propagation for Spatial AIPreprint

Davison, A. J., & Ortiz, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.14139 · Cited by 39

A Survey and Perspective on Artificial Intelligence for Security-Aware Electronic Design Automation

Koblah, D. S., Acharya, R. Y., Capecci, D. E., Dizon-Paradis, O. P., Tajik, S., Ganji, F., Woodard, D. L., & Forte, D. J. (2022) · ACM Transactions on Design Automation of Electronic Systems · doi:10.1145/3563391 · Cited by 33

Concept Graph Neural Networks for Surgical Video Understanding

Ban, Y., Eckhoff, J. A., Ward, T. M., Hashimoto, D. A., Meireles, O. R., Rus, D. L., & Rosman, G. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3299518 · Cited by 36

Limits of Depth: Over-Smoothing and Over-Squashing in GNNs

din, A. M. U., & Qureshi, S. (2023) · Big Data Mining and Analytics · doi:10.26599/bdma.2023.9020019 · Cited by 32

Graph convolutional networks for street network analysis with a case study of urban polycentricity in Chinese cities

Ma, D., He, F., Yue, Y., Guo, R., Zhao, T., & Wang, M. (2024) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2024.2321229 · Cited by 31

Hybrid modeling design patterns

Rudolph, M., Kurz, S., & Rakitsch, B. (2024) · Journal of Mathematics in Industry · doi:10.1186/s13362-024-00141-0 · Cited by 44

MARBLE: interpretable representations of neural population dynamics using geometric deep learning

Gosztolai, A., Peach, R. L., Arnaudon, A., Barahona, M., & Vandergheynst, P. (2025) · Nature Methods · doi:10.1038/s41592-024-02582-2 · Cited by 36

Topology-Aware Graph Neural Network-Based State Estimation for PMU-Unobservable Power Systems

Moshtagh, S., Azimian, B., Golgol, M., & Pal, A. (2025) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2025.3572848 · Cited by 31

Registration-Free Infant Cortical Surface Parcellation Using Deep Convolutional Neural Networks

Wu, Z., Li, G., Wang, L., Shi, F., Lin, W., Gilmore, J. H., & Shen, D. G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00931-1_77 · Cited by 31

Efficient Relative Attribute Learning Using Graph Neural Networks

Meng, Z., Adluru, N., Kim, H. J., Fung, G., & Singh, V. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_34 · Cited by 32

Wasserstein of Wasserstein Loss for Learning Generative Models

Dukler, Y., Li, W., Lin, A. T., & Montúfar, G. (2019) · eScholarship (California Digital Library) · Cited by 31

Graph Neural Networks for Predicting Protein Functions

Ioannidis, V. N., Marques, A. G., & Giannakis, G. B. (2019) · IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing · doi:10.1109/camsap45676.2019.9022646 · Cited by 32

RA-GCN

Tian, A., Zhang, C., Rang, M., Yang, X., & Zhan, Z. (2020) · International Conference on Machine Learning and Computing (ICMLC) · doi:10.1145/3383972.3384067 · Cited by 31

Adaptive estimation and sparse sampling for graph signals in alpha-stable noise

Nguyen, N. H., Doğançay, K., & Wang, W. (2020) · Digital Signal Processing · doi:10.1016/j.dsp.2020.102782 · Cited by 31

Distributed Training of Graph Convolutional Networks

Scardapane, S., Spinelli, I., & Lorenzo, P. D. (2020) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2020.3046237 · Cited by 34

R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning

Li, I., Fabbri, A. R., Hingmire, S., & Radev, D. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.99 · Cited by 34

GNNMark: A Benchmark Suite to Characterize Graph Neural Network Training on GPUs

Baruah, T., Shivdikar, K., Dong, S., Sun, Y., Mojumder, S. A., Jung, K., Abellán, J. L., Ukidave, Y., Joshi, A., Kim, J., et al. (2021) · IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) · doi:10.1109/ispass51385.2021.00013 · Cited by 34

Multitask Shape Optimization Using a 3-D Point Cloud Autoencoder as Unified Representation

Rios, T., van Stein, B., Bäck, T., Sendhoff, B., & Menzel, S. (2021) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2021.3086308 · Cited by 35

Sparse to Dense Dynamic 3D Facial Expression Generation

Otberdout, N., Ferrari, C., Daoudi, M., Berretti, S., & Bimbo, A. D. (2022) · 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/cvpr52688.2022.01974 · Cited by 31

Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing

Wang, L., Qin, B., Hui, B., Li, B., Yang, M., Wang, B., Li, B., Sun, J., Huang, F., Si, L., et al. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539305 · Cited by 33

Simultaneous Inference of Past Demography and Selection from the Ancestral Recombination Graph under the Beta Coalescent

Korfmann, K., Sellinger, T. P. P., Freund, F., Fumagalli, M., & Tellier, A. (2024) · Peer Community Journal · doi:10.24072/pcjournal.397 · Cited by 34

DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks

Liu, F., Feng, J. H., Chen, G., Wu, Y., Hong, Y., Yap, P., & Shen, D. G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-35817-4_11 · Cited by 31

Direct Feedback Alignment Scales to Modern Deep Learning Tasks and ArchitecturesPreprint

Launay, J., Poli, I., Boniface, F., & Krząkała, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.12878 · Cited by 37

Equivariant Subgraph Aggregation NetworksPreprint

Bevilacqua, B., Frasca, F., Lim, D., Srinivasan, B., Cai, C., Balamurugan, G., Bronstein, M. M., & Maron, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.02910 · Cited by 82

Deep Representation Learning: Fundamentals, Technologies, Applications, and Open Challenges

Payandeh, A., Baghaei, K. T., Fayyazsanavi, P., Ramezani, S. B., Chen, Z., & Rahimi, S. (2023) · IEEE Access · doi:10.1109/access.2023.3335196 · Cited by 49

From dense point clouds to semantic digital models: End-to-end AI-based automation procedure for Manhattan-world structures

Mehranfar, M., Braun, A., & Borrmann, A. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105392 · Cited by 31

ContractGNN: Ethereum Smart Contract Vulnerability Detection Based on Vulnerability Sub-Graphs and Graph Neural Networks

Wang, Y., Zhao, X., He, L., Zhen, Z., & Chen, H. (2024) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2024.3470788 · Cited by 32

Learning temporal attention in dynamic graphs with bilinear interactions

Knyazev, B. A., Augusta, C., & Taylor, G. W. (2021) · PLoS ONE · doi:10.1371/journal.pone.0247936 · Cited by 30

Deep Graph Mapper: Seeing Graphs Through the Neural Lens

Bodnar, C., Cangea, C., & Lió, P. (2021) · Frontiers in Big Data · doi:10.3389/fdata.2021.680535 · Cited by 30

Shape correspondence using anisotropic Chebyshev spectral CNNs

Li, Q., Liu, S., Hu, L., & Liu, X. (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/cvpr42600.2020.01467 · Cited by 30

Ontologies, Knowledge Representation, and Machine Learning for Translational Research: Recent Contributions

Robinson, P. N., & Haendel, M. A. (2020) · Yearbook of Medical Informatics · doi:10.1055/s-0040-1701991 · Cited by 30

EdgeConv with Attention Module for Monocular Depth Estimation

Lee, M., Hwang, S., Park, C., & Lee, S. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00242 · Cited by 30

Assessing IFC classes with means of geometric deep learning on different graph encodings

Collins, F. C., Braun, A., Ringsquandl, M., Hall, D., & Borrmann, A. (2021) · Computing in construction · doi:10.35490/ec3.2021.168 · Cited by 30

A Practical Tutorial on Graph Neural Networks

Ward, I. R., Joyner, J., Lickfold, C., Guo, Y., & Bennamoun, M. (2022) · ACM Computing Surveys · doi:10.1145/3503043 · Cited by 43

Long time series ozone prediction in China: A novel dynamic spatiotemporal deep learning approach

Mao, W., Jiao, L., & Wang, W. (2022) · Building and Environment · doi:10.1016/j.buildenv.2022.109087 · Cited by 29

Data-driven emergence of convolutional structure in neural networks

Ingrosso, A., & Goldt, S. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2201854119 · Cited by 34

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