{"total":19,"rows":[{"id":"2103.03212","title":"Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks","created":"2021-03-04","cited_by_count":221,"area":null},{"id":"2301.12082","title":"Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore","created":"2023-01-28","cited_by_count":75,"area":null},{"id":"2102.06986","title":"How Framelets Enhance Graph Neural Networks","created":"2021-02-13","cited_by_count":46,"area":null},{"id":"2206.07922","title":"Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications","created":"2022-06-16","cited_by_count":30,"area":null},{"id":"2105.01563","title":"Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action Recognition","created":"2021-05-04","cited_by_count":22,"area":null},{"id":"2306.04265","title":"Permutation Equivariant Graph Framelets for Heterophilous Graph Learning","created":"2023-06-07","cited_by_count":19,"area":null},{"id":"2109.09426","title":"A Meta-Learning Approach for Training Explainable Graph Neural Networks","created":"2021-09-20","cited_by_count":15,"area":null},{"id":"2210.04124","title":"Generalized energy and gradient flow via graph framelets","created":"2022-10-08","cited_by_count":10,"area":null},{"id":"2108.01660","title":"Graph Neural Networks With Lifting-based Adaptive Graph Wavelets","created":"2021-08-03","cited_by_count":6,"area":null},{"id":"2110.04824","title":"Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks","created":"2021-10-10","cited_by_count":5,"area":null},{"id":"2111.07602","title":"Spectral Transform Forms Scalable Transformer","created":"2021-11-15","cited_by_count":4,"area":null},{"id":"2302.14806","title":"Framelet Message Passing","created":"2023-02-28","cited_by_count":3,"area":null},{"id":"2311.05767","title":"Dirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation","created":"2023-11-09","cited_by_count":3,"area":null},{"id":"2312.05479","title":"Exploring Sparsity in Graph Transformers","created":"2023-12-09","cited_by_count":3,"area":null},{"id":"2206.00979","title":"Multi-scale Wasserstein Shortest-path Graph Kernels for Graph Classification","created":"2022-06-02","cited_by_count":1,"area":null},{"id":"2210.02604","title":"Spectral Regularization Allows Data-frugal Learning over Combinatorial Spaces","created":"2022-10-05","cited_by_count":1,"area":null},{"id":"2305.15639","title":"Revisiting Generalized p-Laplacian Regularized Framelet GCNs: Convergence, Energy Dynamic and Training with Non-Linear Diffusion","created":"2023-05-25","cited_by_count":1,"area":null},{"id":"2111.08228","title":"SStaGCN: Simplified stacking based graph convolutional networks","created":"2021-11-16","cited_by_count":0,"area":null},{"id":"2212.06423","title":"Coarse-to-Fine Contrastive Learning on Graphs","created":"2022-12-13","cited_by_count":0,"area":null}],"next_cursor":null}