{"list":"other","total":58,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"1710.10903","title":"Graph Attention Networks","created":"2017-10-30","cited_by_count":8923},{"id":"1810.00826","title":"How Powerful are Graph Neural Networks?","created":"2018-10-01","cited_by_count":4393},{"id":"2004.11362","title":"Supervised Contrastive Learning","created":"2020-04-23","cited_by_count":2829},{"id":"1903.02428","title":"Fast Graph Representation Learning with PyTorch Geometric","created":"2019-03-06","cited_by_count":2434},{"id":"1606.09375","title":"Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering","created":"2016-06-30","cited_by_count":2089},{"id":"2104.13478","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","created":"2021-04-27","cited_by_count":1167},{"id":"1312.6203","title":"Spectral Networks and Locally Connected Networks on Graphs","created":"2013-12-21","cited_by_count":1102},{"id":"1611.08097","title":"Geometric deep learning: going beyond Euclidean data","created":"2016-11-24","cited_by_count":1048},{"id":"2102.09844","title":"E(n) Equivariant Graph Neural Networks","created":"2021-02-19","cited_by_count":965},{"id":"1702.03118","title":"Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning","created":"2017-02-10","cited_by_count":950},{"id":"1806.03536","title":"Representation Learning on Graphs with Jumping Knowledge Networks","created":"2018-06-09","cited_by_count":877},{"id":"1802.08219","title":"Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds","created":"2018-02-22","cited_by_count":746},{"id":"2101.03164","title":"E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials","created":"2021-01-08","cited_by_count":708},{"id":"1712.00468","title":"Graph Signal Processing: Overview, Challenges and Applications","created":"2017-12-01","cited_by_count":413},{"id":"1905.09550","title":"Revisiting Graph Neural Networks: All We Have is Low-Pass Filters","created":"2019-05-23","cited_by_count":261},{"id":"2204.00673","title":"Learnable latent embeddings for joint behavioral and neural analysis","created":"2022-04-01","cited_by_count":117},{"id":"2301.09308","title":"On the Expressive Power of Geometric Graph Neural Networks","created":"2023-01-23","cited_by_count":90},{"id":"2312.07511","title":"A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems","created":"2023-12-12","cited_by_count":68},{"id":"1812.11214","title":"Kymatio: Scattering Transforms in Python","created":"2018-12-28","cited_by_count":58},{"id":"2202.07230","title":"Geometrically Equivariant Graph Neural Networks: A Survey","created":"2022-02-15","cited_by_count":56},{"id":"1810.03068","title":"Geometric Scattering for Graph Data Analysis","created":"2018-10-07","cited_by_count":49},{"id":"1512.06293","title":"A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction","created":"2015-12-19","cited_by_count":46},{"id":"1804.00099","title":"Graph Convolutional Neural Networks via Scattering","created":"2018-03-31","cited_by_count":46},{"id":"1806.08829","title":"Diffusion Scattering Transforms on Graphs","created":"2018-06-22","cited_by_count":44},{"id":"1703.08961","title":"Scaling the Scattering Transform: Deep Hybrid Networks","created":"2017-03-27","cited_by_count":35},{"id":"2206.01506","title":"Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem?","created":"2022-06-03","cited_by_count":25},{"id":"2303.11323","title":"Tangent Bundle Convolutional Learning: from Manifolds to Cellular Sheaves and Back","created":"2023-03-20","cited_by_count":22},{"id":"1306.1587","title":"Spectral Convergence of the connection Laplacian from random samples","created":"2013-06-07","cited_by_count":19},{"id":"2201.08932","title":"Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks","created":"2022-01-22","cited_by_count":18},{"id":"2208.08561","title":"Geometric Scattering on Measure Spaces","created":"2022-08-17","cited_by_count":17},{"id":"2210.00376","title":"Convolutional Neural Networks on Manifolds: From Graphs and Back","created":"2022-10-01","cited_by_count":14},{"id":"1805.00571","title":"Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties","created":"2018-05-01","cited_by_count":12},{"id":"1911.06253","title":"Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms","created":"2019-11-14","cited_by_count":12},{"id":"2304.03376","title":"Interpretable statistical representations of neural population dynamics and geometry","created":"2023-04-06","cited_by_count":12},{"id":"2312.13032","title":"NodeMixup: Tackling Under-Reaching for Graph Neural Networks","created":"2023-12-20","cited_by_count":12},{"id":"2402.04836","title":"On the Completeness of Invariant Geometric Deep Learning Models","created":"2024-02-07","cited_by_count":11},{"id":"2106.03725","title":"Stability to Deformations of Manifold Filters and Manifold Neural Networks","created":"2021-06-07","cited_by_count":9},{"id":"2110.04702","title":"Stability of Neural Networks on Manifolds to Relative Perturbations","created":"2021-10-10","cited_by_count":9},{"id":"2206.10078","title":"The Manifold Scattering Transform for High-Dimensional Point Cloud Data","created":"2022-06-21","cited_by_count":9},{"id":"2208.07458","title":"Learnable Filters for Geometric Scattering Modules","created":"2022-08-15","cited_by_count":9},{"id":"2405.20543","title":"Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNs","created":"2024-05-31","cited_by_count":9},{"id":"1905.10448","title":"Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds","created":"2019-05-24","cited_by_count":7},{"id":"2110.06241","title":"Molecular Graph Generation via Geometric Scattering","created":"2021-10-12","cited_by_count":7},{"id":"1504.05487","title":"Deep Convolutional Neural Networks Based on Semi-Discrete Frames","created":"2015-04-21","cited_by_count":6},{"id":"2212.12606","title":"A Convergence Rate for Manifold Neural Networks","created":"2022-12-23","cited_by_count":6},{"id":"2409.05191","title":"Generalization of Geometric Graph Neural Networks with Lipschitz Loss Functions","created":"2024-09-08","cited_by_count":6},{"id":"2010.15010","title":"Geometric Scattering Attention Networks","created":"2020-10-28","cited_by_count":5},{"id":"2301.02136","title":"Multiscale Transforms for Signals on Simplicial Complexes","created":"2022-12-28","cited_by_count":4},{"id":"2409.09469","title":"Hyperedge Representations with Hypergraph Wavelets: Applications to Spatial Transcriptomics","created":"2024-09-14","cited_by_count":4},{"id":"2508.02723","title":"Mathematical Foundations of Geometric Deep Learning","created":"2025-08-01","cited_by_count":4},{"id":"2009.09020","title":"Natural Graph Wavelet Packet Dictionaries","created":"2020-09-18","cited_by_count":3},{"id":"2309.09924","title":"DYMAG: Rethinking Message Passing Using Dynamical-systems-based Waveforms","created":"2023-09-18","cited_by_count":3},{"id":"2310.17579","title":"BLIS-Net: Classifying and Analyzing Signals on Graphs","created":"2023-10-26","cited_by_count":3},{"id":"2307.04056","title":"Manifold Filter-Combine Networks","created":"2023-07-08","cited_by_count":2},{"id":"2309.07813","title":"Directed Scattering for Knowledge Graph-based Cellular Signaling Analysis","created":"2023-09-14","cited_by_count":2},{"id":"2502.07746","title":"HiPoNet: A Multi-View Simplicial Complex Network for High Dimensional Point-Cloud and Single-Cell Data","created":"2025-02-11","cited_by_count":2}],"next_cursor":null}