{"list":"other","total":52,"rows":[{"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":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1901.00596","title":"A Comprehensive Survey on Graph Neural Networks","created":"2019-01-03","cited_by_count":2919},{"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":"1902.07153","title":"Simplifying Graph Convolutional Networks","created":"2019-02-19","cited_by_count":1477},{"id":"1810.05997","title":"Predict then Propagate: Graph Neural Networks meet Personalized PageRank","created":"2018-10-14","cited_by_count":993},{"id":"2203.11932","title":"Dataset Distillation by Matching Training Trajectories","created":"2022-03-22","cited_by_count":442},{"id":"2103.09430","title":"OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs","created":"2021-03-17","cited_by_count":329},{"id":"2004.11198","title":"SIGN: Scalable Inception Graph Neural Networks","created":"2020-04-23","cited_by_count":284},{"id":"2006.02903","title":"A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions","created":"2020-06-01","cited_by_count":254},{"id":"2011.08843","title":"Design Space for Graph Neural Networks","created":"2020-11-17","cited_by_count":182},{"id":"2106.06134","title":"Is Homophily a Necessity for Graph Neural Networks?","created":"2021-06-11","cited_by_count":182},{"id":"2208.09481","title":"Graph neural networks for materials science and chemistry","created":"2022-08-05","cited_by_count":182},{"id":"2202.07082","title":"Graph Neural Networks for Graphs with Heterophily: A Survey","created":"2022-02-14","cited_by_count":175},{"id":"1905.13192","title":"Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels","created":"2019-05-30","cited_by_count":161},{"id":"2303.00654","title":"How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy","created":"2023-03-01","cited_by_count":153},{"id":"2110.07580","title":"Graph Condensation for Graph Neural Networks","created":"2021-10-14","cited_by_count":150},{"id":"2306.10759","title":"SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations","created":"2023-06-19","cited_by_count":133},{"id":"2301.07014","title":"Dataset Distillation: A Comprehensive Review","created":"2023-01-17","cited_by_count":127},{"id":"2110.14038","title":"Robustness of Graph Neural Networks at Scale","created":"2021-10-26","cited_by_count":100},{"id":"2206.07746","title":"Condensing Graphs via One-Step Gradient Matching","created":"2022-06-15","cited_by_count":100},{"id":"2302.14416","title":"DREAM: Efficient Dataset Distillation by Representative Matching","created":"2023-02-28","cited_by_count":94},{"id":"2301.04272","title":"Data Distillation: A Survey","created":"2023-01-11","cited_by_count":87},{"id":"2010.00906","title":"Quantifying Privacy Leakage in Graph Embedding","created":"2020-10-02","cited_by_count":82},{"id":"2109.05641","title":"Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?","created":"2021-09-12","cited_by_count":82},{"id":"2106.05150","title":"Scaling Up Graph Neural Networks Via Graph Coarsening","created":"2021-06-09","cited_by_count":80},{"id":"2005.06149","title":"DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses","created":"2020-05-13","cited_by_count":78},{"id":"2306.02664","title":"Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data","created":"2023-06-05","cited_by_count":71},{"id":"2207.09639","title":"DC-BENCH: Dataset Condensation Benchmark","created":"2022-07-20","cited_by_count":67},{"id":"2206.13697","title":"Graph Condensation via Receptive Field Distribution Matching","created":"2022-06-28","cited_by_count":60},{"id":"2402.03358","title":"A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation","created":"2024-01-29","cited_by_count":57},{"id":"2309.09455","title":"CaT: Balanced Continual Graph Learning with Graph Condensation","created":"2023-09-18","cited_by_count":52},{"id":"2207.04869","title":"Graph-based Molecular Representation Learning","created":"2022-07-08","cited_by_count":51},{"id":"2310.09192","title":"Does Graph Distillation See Like Vision Dataset Counterpart?","created":"2023-10-13","cited_by_count":45},{"id":"2310.11046","title":"Fast Graph Condensation with Structure-based Neural Tangent Kernel","created":"2023-10-17","cited_by_count":36},{"id":"2403.19852","title":"A Review of Graph Neural Networks in Epidemic Modeling","created":"2024-03-28","cited_by_count":31},{"id":"2402.05011","title":"Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching","created":"2024-02-07","cited_by_count":28},{"id":"2209.07807","title":"Model Inversion Attacks against Graph Neural Networks","created":"2022-09-16","cited_by_count":26},{"id":"2401.11720","title":"Graph Condensation: A Survey","created":"2024-01-22","cited_by_count":23},{"id":"2308.16375","title":"A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications","created":"2023-08-31","cited_by_count":22},{"id":"2402.05962","title":"EXGC: Bridging Efficiency and Explainability in Graph Condensation","created":"2024-02-05","cited_by_count":20},{"id":"2306.14052","title":"A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware","created":"2023-06-24","cited_by_count":18},{"id":"2207.06221","title":"Collaboration-Aware Graph Convolutional Network for Recommender Systems","created":"2022-07-03","cited_by_count":12},{"id":"2402.02000","title":"A Survey on Graph Condensation","created":"2024-02-03","cited_by_count":9},{"id":"2305.17386","title":"HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer","created":"2023-05-27","cited_by_count":8},{"id":"1911.01670","title":"RobustECD: Enhancement of Network Structure for Robust Community Detection","created":"2019-11-05","cited_by_count":7},{"id":"2309.09517","title":"FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks","created":"2023-09-18","cited_by_count":7},{"id":"2402.09565","title":"Graph-Skeleton: ~1% Nodes are Sufficient to Represent Billion-Scale Graph","created":"2024-02-14","cited_by_count":6},{"id":"2405.14246","title":"GCondenser: Benchmarking Graph Condensation","created":"2024-05-23","cited_by_count":5},{"id":"2405.17404","title":"Spectral Greedy Coresets for Graph Neural Networks","created":"2024-05-27","cited_by_count":3}],"next_cursor":null}