{"total":54,"rows":[{"id":"2102.09844","title":"E(n) Equivariant Graph Neural Networks","created":"2021-02-19","cited_by_count":965,"area":null},{"id":"2106.08903","title":"GemNet: Universal Directional Graph Neural Networks for Molecules","created":"2021-06-02","cited_by_count":343,"area":null},{"id":"2101.10320","title":"Identity-aware Graph Neural Networks","created":"2021-01-25","cited_by_count":198,"area":null},{"id":"2110.03336","title":"Frame Averaging for Invariant and Equivariant Network Design","created":"2021-10-07","cited_by_count":168,"area":null},{"id":"2110.02910","title":"Equivariant Subgraph Aggregation Networks","created":"2021-10-06","cited_by_count":154,"area":null},{"id":"2106.06610","title":"Scalars are universal: Equivariant machine learning, structured like classical physics","created":"2021-06-11","cited_by_count":127,"area":null},{"id":"2206.00133","title":"Pre-training via Denoising for Molecular Property Prediction","created":"2022-05-31","cited_by_count":124,"area":null},{"id":"2112.09992","title":"Weisfeiler and Leman go Machine Learning: The Story so far","created":"2021-12-18","cited_by_count":122,"area":null},{"id":"2206.11140","title":"Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries","created":"2022-06-22","cited_by_count":112,"area":null},{"id":"2106.02347","title":"SE(3)-equivariant prediction of molecular wavefunctions and electronic densities","created":"2021-06-04","cited_by_count":67,"area":null},{"id":"2204.07697","title":"Theory of Graph Neural Networks: Representation and Learning","created":"2022-04-16","cited_by_count":61,"area":null},{"id":"2106.07971","title":"Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond","created":"2021-06-15","cited_by_count":44,"area":null},{"id":"2211.15420","title":"Equivariant Networks for Crystal Structures","created":"2022-11-15","cited_by_count":24,"area":null},{"id":"2106.00967","title":"Multiresolution Equivariant Graph Variational Autoencoder","created":"2021-06-02","cited_by_count":20,"area":null},{"id":"2209.03416","title":"Bispectral Neural Networks","created":"2022-09-07","cited_by_count":18,"area":null},{"id":"2106.06662","title":"Equivariant Networks for Pixelized Spheres","created":"2021-06-12","cited_by_count":16,"area":null},{"id":"2205.14368","title":"Going Deeper into Permutation-Sensitive Graph Neural Networks","created":"2022-05-28","cited_by_count":15,"area":null},{"id":"2104.14060","title":"WGCN: Graph Convolutional Networks with Weighted Structural Features","created":"2021-04-29","cited_by_count":12,"area":null},{"id":"2203.01216","title":"A Simple and Universal Rotation Equivariant Point-cloud Network","created":"2022-03-02","cited_by_count":11,"area":null},{"id":"2205.14831","title":"Temporal Multiresolution Graph Neural Networks For Epidemic Prediction","created":"2022-05-30","cited_by_count":11,"area":null},{"id":"2401.09235","title":"A Characterization Theorem for Equivariant Networks with Point-wise Activations","created":"2024-01-17","cited_by_count":9,"area":null},{"id":"2402.04821","title":"E(3)-Equivariant Mesh Neural Networks","created":"2024-02-07","cited_by_count":9,"area":null},{"id":"2406.19244","title":"Improving the Expressiveness of $K$-hop Message-Passing GNNs by Injecting Contextualized Substructure Information","created":"2024-06-27","cited_by_count":9,"area":null},{"id":"2404.00477","title":"DE-HNN: An effective neural model for Circuit Netlist representation","created":"2024-03-30","cited_by_count":7,"area":null},{"id":"2409.11772","title":"Symmetry-Based Structured Matrices for Efficient Approximately Equivariant Networks","created":"2024-09-18","cited_by_count":7,"area":null},{"id":"2305.07731","title":"Predicting COVID-19 pandemic by spatio-temporal graph neural networks: A New Zealand's study","created":"2023-05-12","cited_by_count":6,"area":null},{"id":"2406.19832","title":"MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph Classification","created":"2024-06-28","cited_by_count":5,"area":"distill"},{"id":"2110.08092","title":"Equivariant and Invariant Reynolds Networks","created":"2021-10-15","cited_by_count":4,"area":null},{"id":"2206.10690","title":"Learning Continuous Rotation Canonicalization with Radial Beam Sampling","created":"2022-06-21","cited_by_count":4,"area":null},{"id":"2302.08647","title":"Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical Structures","created":"2023-02-17","cited_by_count":4,"area":null},{"id":"2109.06861","title":"Nonlinearities in Steerable SO(2)-Equivariant CNNs","created":"2021-09-14","cited_by_count":3,"area":null},{"id":"2209.06905","title":"Graph Neural Network Based Node Deployment for Throughput Enhancement","created":"2022-08-19","cited_by_count":3,"area":null},{"id":"2411.10509","title":"TESGNN: Temporal Equivariant Scene Graph Neural Networks for Efficient and Robust Multi-View 3D Scene Understanding","created":"2024-11-15","cited_by_count":3,"area":null},{"id":"2103.00142","title":"Disentangling Geometric Deformation Spaces in Generative Latent Shape Models","created":"2021-02-27","cited_by_count":2,"area":null},{"id":"2110.07472","title":"Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?","created":"2021-10-14","cited_by_count":2,"area":null},{"id":"2111.11840","title":"Subgraph Permutation Equivariant Networks","created":"2021-11-23","cited_by_count":2,"area":null},{"id":"2206.06758","title":"Universally Expressive Communication in Multi-Agent Reinforcement Learning","created":"2022-06-14","cited_by_count":2,"area":null},{"id":"2311.10808","title":"Multiparameter Persistent Homology for Molecular Property Prediction","created":"2023-11-17","cited_by_count":2,"area":null},{"id":"2401.00744","title":"Towards Harmonization of SO(3)-Equivariance and Expressiveness: a Hybrid Deep Learning Framework for Electronic-Structure Hamiltonian Prediction","created":"2024-01-01","cited_by_count":2,"area":null},{"id":"2406.03017","title":"DifAttack++: Query-Efficient Black-Box Adversarial Attack via Hierarchical Disentangled Feature Space in Cross-Domain","created":"2024-06-05","cited_by_count":2,"area":null},{"id":"2504.02763","title":"CanonNet: Canonical Ordering and Curvature Learning for Point Cloud Analysis","created":"2025-04-03","cited_by_count":2,"area":null},{"id":"2111.01940","title":"Learning Multiresolution Matrix Factorization and its Wavelet Networks on Graphs","created":"2021-11-02","cited_by_count":1,"area":null},{"id":"2312.07633","title":"SE(3)-Invariant Multiparameter Persistent Homology for Chiral-Sensitive Molecular Property Prediction","created":"2023-12-12","cited_by_count":1,"area":null},{"id":"2402.15413","title":"G-RepsNet: A Fast and General Construction of Equivariant Networks for Arbitrary Matrix Groups","created":"2024-02-23","cited_by_count":1,"area":null},{"id":"2404.18538","title":"Symmetry group based domain decomposition to enhance physics-informed neural networks for solving partial differential equations","created":"2024-04-29","cited_by_count":1,"area":null},{"id":"2205.15555","title":"Graph-level Neural Networks: Current Progress and Future Directions","created":"2022-05-31","cited_by_count":0,"area":null},{"id":"2310.15543","title":"Symmetry-preserving graph attention network to solve routing problems at multiple resolutions","created":"2023-10-24","cited_by_count":0,"area":null},{"id":"2408.05822","title":"Sampling Foundational Transformer: A Theoretical Perspective","created":"2024-08-11","cited_by_count":0,"area":null},{"id":"2409.19117","title":"Range-aware Positional Encoding via High-order Pretraining: Theory and Practice","created":"2024-09-27","cited_by_count":0,"area":null},{"id":"2412.17468","title":"Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation Learning","created":"2024-12-23","cited_by_count":0,"area":null},{"id":"2502.16289","title":"MOB-GCN: A Novel Multiscale Object-Based Graph Neural Network for Hyperspectral Image Classification","created":"2025-02-22","cited_by_count":0,"area":null},{"id":"2505.12360","title":"LaPON: A Lagrange's-mean-value-theorem-inspired operator network for solving PDEs and its application on NSE","created":"2025-05-18","cited_by_count":0,"area":null},{"id":"2509.24472","title":"FS-KAN: Permutation Equivariant Kolmogorov-Arnold Networks via Function Sharing","created":"2025-09-29","cited_by_count":0,"area":null},{"id":"2609.18704","title":"Toward Composable Network Digital Twins: A Subgraph-Based Latency Prediction Study","created":"2026-09-16","cited_by_count":0,"area":null}],"next_cursor":null}