{"list":"other","total":55,"rows":[{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"1710.10903","title":"Graph Attention Networks","created":"2017-10-30","cited_by_count":8923},{"id":"1602.05629","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","created":"2016-02-17","cited_by_count":7669},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1812.06127","title":"Federated Optimization in Heterogeneous Networks","created":"2018-12-14","cited_by_count":2987},{"id":"1907.10902","title":"Optuna: A Next-generation Hyperparameter Optimization Framework","created":"2019-07-25","cited_by_count":2501},{"id":"2005.00687","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","created":"2020-05-02","cited_by_count":2163},{"id":"1703.06103","title":"Modeling Relational Data with Graph Convolutional Networks","created":"2017-03-17","cited_by_count":1694},{"id":"1902.07153","title":"Simplifying Graph Convolutional Networks","created":"2019-02-19","cited_by_count":1477},{"id":"1906.08935","title":"Deep Leakage from Gradients","created":"2019-06-21","cited_by_count":1021},{"id":"1811.05868","title":"Pitfalls of Graph Neural Network Evaluation","created":"2018-11-14","cited_by_count":1007},{"id":"1603.08861","title":"Revisiting Semi-Supervised Learning with Graph Embeddings","created":"2016-03-29","cited_by_count":976},{"id":"2007.02133","title":"Simple and Deep Graph Convolutional Networks","created":"2020-07-04","cited_by_count":860},{"id":"2002.05287","title":"Geom-GCN: Geometric Graph Convolutional Networks","created":"2020-02-13","cited_by_count":826},{"id":"2103.16257","title":"Model-Contrastive Federated Learning","created":"2021-03-30","cited_by_count":766},{"id":"1903.07293","title":"Heterogeneous Graph Attention Network","created":"2019-03-18","cited_by_count":734},{"id":"2003.14053","title":"Inverting Gradients -- How easy is it to break privacy in federated learning?","created":"2020-03-31","cited_by_count":712},{"id":"2003.01332","title":"Heterogeneous Graph Transformer","created":"2020-03-03","cited_by_count":582},{"id":"2102.02079","title":"Federated Learning on Non-IID Data Silos: An Experimental Study","created":"2021-02-03","cited_by_count":568},{"id":"2109.02846","title":"Datasets: A Community Library for Natural Language Processing","created":"2021-09-07","cited_by_count":530},{"id":"1905.05178","title":"Graph U-Nets","created":"2019-05-11","cited_by_count":492},{"id":"1611.08402","title":"Geometric deep learning on graphs and manifolds using mixture model CNNs","created":"2016-11-25","cited_by_count":468},{"id":"1904.08082","title":"Self-Attention Graph Pooling","created":"2019-04-17","cited_by_count":421},{"id":"1907.09693","title":"A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection","created":"2019-07-23","cited_by_count":374},{"id":"2106.06843","title":"Federated Learning on Non-IID Data: A Survey","created":"2021-06-12","cited_by_count":346},{"id":"2110.14446","title":"Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods","created":"2021-10-27","cited_by_count":311},{"id":"2002.01680","title":"MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding","created":"2020-02-05","cited_by_count":304},{"id":"1802.06893","title":"Learning Word Vectors for 157 Languages","created":"2018-02-19","cited_by_count":280},{"id":"1902.08412","title":"Adversarial Attacks on Graph Neural Networks via Meta Learning","created":"2019-02-22","cited_by_count":261},{"id":"2106.06134","title":"Is Homophily a Necessity for Graph Neural Networks?","created":"2021-06-11","cited_by_count":182},{"id":"2203.11751","title":"FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction","created":"2022-03-22","cited_by_count":181},{"id":"2112.14936","title":"Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks","created":"2021-12-30","cited_by_count":176},{"id":"2202.07082","title":"Graph Neural Networks for Graphs with Heterophily: A Survey","created":"2022-02-14","cited_by_count":175},{"id":"2106.13430","title":"Subgraph Federated Learning with Missing Neighbor Generation","created":"2021-06-25","cited_by_count":132},{"id":"2106.13423","title":"Federated Graph Classification over Non-IID Graphs","created":"2021-06-25","cited_by_count":114},{"id":"2010.10152","title":"Feature Inference Attack on Model Predictions in Vertical Federated Learning","created":"2020-10-20","cited_by_count":106},{"id":"1710.04954","title":"PCPNET: Learning Local Shape Properties from Raw Point Clouds","created":"2017-10-13","cited_by_count":98},{"id":"2211.13009","title":"Federated Learning on Non-IID Graphs via Structural Knowledge Sharing","created":"2022-11-23","cited_by_count":88},{"id":"2010.15421","title":"Scalable Graph Neural Networks via Bidirectional Propagation","created":"2020-10-29","cited_by_count":87},{"id":"2206.04355","title":"Graph Attention Multi-Layer Perceptron","created":"2022-06-09","cited_by_count":83},{"id":"2104.14362","title":"From Distributed Machine Learning to Federated Learning: A Survey","created":"2021-04-29","cited_by_count":66},{"id":"2401.03230","title":"FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning","created":"2024-01-06","cited_by_count":47},{"id":"2009.05537","title":"Federated Model Distillation with Noise-Free Differential Privacy","created":"2020-09-11","cited_by_count":40},{"id":"2006.16811","title":"Path Integral Based Convolution and Pooling for Graph Neural Networks","created":"2020-06-29","cited_by_count":35},{"id":"2105.03170","title":"FedGL: Federated Graph Learning Framework with Global Self-Supervision","created":"2021-05-07","cited_by_count":34},{"id":"2206.09888","title":"SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression","created":"2022-06-20","cited_by_count":34},{"id":"2104.00919","title":"Fast-adapting and Privacy-preserving Federated Recommender System","created":"2021-04-02","cited_by_count":30},{"id":"2406.18937","title":"Federated Graph Semantic and Structural Learning","created":"2024-06-27","cited_by_count":30},{"id":"2311.03154","title":"Convergence Analysis of Sequential Federated Learning on Heterogeneous Data","created":"2023-11-06","cited_by_count":23},{"id":"2307.12810","title":"HeteFedRec: Federated Recommender Systems with Model Heterogeneity","created":"2023-07-24","cited_by_count":20},{"id":"2310.11730","title":"Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation","created":"2023-10-18","cited_by_count":19},{"id":"2311.05304","title":"Data Valuation and Detections in Federated Learning","created":"2023-11-09","cited_by_count":17},{"id":"2401.11772","title":"LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning","created":"2024-01-22","cited_by_count":11},{"id":"2211.13170","title":"The World of Graph Databases from An Industry Perspective","created":"2022-11-23","cited_by_count":7},{"id":"2303.00492","title":"Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices","created":"2023-03-01","cited_by_count":6}],"next_cursor":null}