{"list":"other","total":160,"rows":[{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"1602.05629","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","created":"2016-02-17","cited_by_count":7669},{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1602.04938","title":"\"Why Should I Trust You?\": Explaining the Predictions of Any Classifier","created":"2016-02-16","cited_by_count":5070},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"1703.01365","title":"Axiomatic Attribution for Deep Networks","created":"2017-03-04","cited_by_count":3384},{"id":"1912.04977","title":"Advances and Open Problems in Federated Learning","created":"2019-12-10","cited_by_count":3046},{"id":"1607.00133","title":"Deep Learning with Differential Privacy","created":"2016-07-01","cited_by_count":2728},{"id":"1610.05820","title":"Membership Inference Attacks against Machine Learning Models","created":"2016-10-18","cited_by_count":2281},{"id":"1610.02413","title":"Equality of Opportunity in Supervised Learning","created":"2016-10-07","cited_by_count":2007},{"id":"1908.09635","title":"A Survey on Bias and Fairness in Machine Learning","created":"2019-08-23","cited_by_count":1917},{"id":"1703.06103","title":"Modeling Relational Data with Graph Convolutional Networks","created":"2017-03-17","cited_by_count":1694},{"id":"1812.08434","title":"Graph Neural Networks: A Review of Methods and Applications","created":"2018-12-20","cited_by_count":1576},{"id":"1610.05492","title":"Federated Learning: Strategies for Improving Communication Efficiency","created":"2016-10-18","cited_by_count":1475},{"id":"2010.13902","title":"Graph Contrastive Learning with Augmentations","created":"2020-10-22","cited_by_count":1208},{"id":"1706.07269","title":"Explanation in Artificial Intelligence: Insights from the Social Sciences","created":"2017-06-22","cited_by_count":1206},{"id":"1810.03292","title":"Sanity Checks for Saliency Maps","created":"2018-10-08","cited_by_count":1156},{"id":"1806.01973","title":"Graph Convolutional Neural Networks for Web-Scale Recommender Systems","created":"2018-06-06","cited_by_count":1138},{"id":"1712.09913","title":"Visualizing the Loss Landscape of Neural Nets","created":"2017-12-28","cited_by_count":1130},{"id":"1703.00564","title":"MoleculeNet: A Benchmark for Molecular Machine Learning","created":"2017-03-02","cited_by_count":1051},{"id":"1811.05868","title":"Pitfalls of Graph Neural Network Evaluation","created":"2018-11-14","cited_by_count":1007},{"id":"1810.05997","title":"Predict then Propagate: Graph Neural Networks meet Personalized PageRank","created":"2018-10-14","cited_by_count":993},{"id":"1412.6806","title":"Striving for Simplicity: The All Convolutional Net","created":"2014-12-21","cited_by_count":990},{"id":"1905.12265","title":"Strategies for Pre-training Graph Neural Networks","created":"2019-05-29","cited_by_count":971},{"id":"1903.03894","title":"GNNExplainer: Generating Explanations for Graph Neural Networks","created":"2019-03-10","cited_by_count":737},{"id":"1903.07293","title":"Heterogeneous Graph Attention Network","created":"2019-03-18","cited_by_count":734},{"id":"2007.08663","title":"TUDataset: A collection of benchmark datasets for learning with graphs","created":"2020-07-16","cited_by_count":728},{"id":"1902.07243","title":"Graph Neural Networks for Social Recommendation","created":"2019-02-19","cited_by_count":714},{"id":"1801.10247","title":"FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling","created":"2018-01-30","cited_by_count":584},{"id":"2003.01332","title":"Heterogeneous Graph Transformer","created":"2020-03-03","cited_by_count":582},{"id":"2006.09963","title":"GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training","created":"2020-06-17","cited_by_count":463},{"id":"2011.04573","title":"Parameterized Explainer for Graph Neural Network","created":"2020-11-09","cited_by_count":426},{"id":"1902.04885","title":"Federated Machine Learning: Concept and Applications","created":"2019-02-13","cited_by_count":412},{"id":"1805.07984","title":"Adversarial Attacks on Neural Networks for Graph Data","created":"2018-05-21","cited_by_count":397},{"id":"2012.15445","title":"Explainability in Graph Neural Networks: A Taxonomic Survey","created":"2020-12-31","cited_by_count":384},{"id":"2005.10203","title":"Graph Structure Learning for Robust Graph Neural Networks","created":"2020-05-20","cited_by_count":371},{"id":"1806.08010","title":"Fairness Without Demographics in Repeated Loss Minimization","created":"2018-06-20","cited_by_count":364},{"id":"1802.06309","title":"Learning Adversarially Fair and Transferable Representations","created":"2018-02-17","cited_by_count":353},{"id":"1610.05755","title":"Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data","created":"2016-10-18","cited_by_count":348},{"id":"1806.08049","title":"On the Robustness of Interpretability Methods","created":"2018-06-21","cited_by_count":314},{"id":"1901.04592","title":"Interpretable machine learning: definitions, methods, and applications","created":"2019-01-14","cited_by_count":305},{"id":"2006.15437","title":"GPT-GNN: Generative Pre-Training of Graph Neural Networks","created":"2020-06-27","cited_by_count":303},{"id":"2201.08164","title":"From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI","created":"2022-01-20","cited_by_count":302},{"id":"1806.02371","title":"Adversarial Attack on Graph Structured Data","created":"2018-06-06","cited_by_count":301},{"id":"2102.05152","title":"On Explainability of Graph Neural Networks via Subgraph Explorations","created":"2021-02-09","cited_by_count":281},{"id":"1902.08412","title":"Adversarial Attacks on Graph Neural Networks via Meta Learning","created":"2019-02-22","cited_by_count":261},{"id":"2006.13114","title":"Fairness without Demographics through Adversarially Reweighted Learning","created":"2020-06-23","cited_by_count":251},{"id":"2006.13009","title":"Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings","created":"2020-06-21","cited_by_count":214},{"id":"2006.02587","title":"XGNN: Towards Model-Level Explanations of Graph Neural Networks","created":"2020-06-03","cited_by_count":211},{"id":"2010.05788","title":"PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks","created":"2020-10-12","cited_by_count":211},{"id":"2001.06216","title":"GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks","created":"2020-01-17","cited_by_count":209},{"id":"2010.00711","title":"A Survey of the State of Explainable AI for Natural Language Processing","created":"2020-10-01","cited_by_count":206},{"id":"1906.04214","title":"Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective","created":"2019-06-10","cited_by_count":196},{"id":"2006.08149","title":"GNNGuard: Defending Graph Neural Networks against Adversarial Attacks","created":"2020-06-15","cited_by_count":195},{"id":"1511.05897","title":"Censoring Representations with an Adversary","created":"2015-11-18","cited_by_count":192},{"id":"2009.01454","title":"Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information","created":"2020-09-03","cited_by_count":168},{"id":"1812.10528","title":"Adversarial Attack and Defense on Graph Data: A Survey","created":"2018-12-26","cited_by_count":167},{"id":"1707.00075","title":"Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations","created":"2017-07-01","cited_by_count":165},{"id":"1905.13686","title":"Explainability Techniques for Graph Convolutional Networks","created":"2019-05-31","cited_by_count":161},{"id":"2009.13295","title":"A Diagnostic Study of Explainability Techniques for Text Classification","created":"2020-09-25","cited_by_count":159},{"id":"1802.00614","title":"Visual Interpretability for Deep Learning: a Survey","created":"2018-02-02","cited_by_count":155},{"id":"2010.00577","title":"Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking","created":"2020-10-01","cited_by_count":153},{"id":"2103.08826","title":"GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks","created":"2021-03-16","cited_by_count":147},{"id":"2007.07646","title":"A Survey of Privacy Attacks in Machine Learning","created":"2020-07-15","cited_by_count":146},{"id":"1905.13662","title":"On the Fairness of Disentangled Representations","created":"2019-05-31","cited_by_count":144},{"id":"2006.03589","title":"Higher-Order Explanations of Graph Neural Networks via Relevant Walks","created":"2020-06-05","cited_by_count":138},{"id":"2103.14991","title":"Graph Unlearning","created":"2021-03-27","cited_by_count":138},{"id":"1801.08284","title":"DKN: Deep Knowledge-Aware Network for News Recommendation","created":"2018-01-25","cited_by_count":137},{"id":"1811.01439","title":"Explaining Explanations in AI","created":"2018-11-04","cited_by_count":137},{"id":"2006.11165","title":"Backdoor Attacks to Graph Neural Networks","created":"2020-06-19","cited_by_count":128},{"id":"2102.13186","title":"Towards a Unified Framework for Fair and Stable Graph Representation Learning","created":"2021-02-25","cited_by_count":128},{"id":"2011.07057","title":"Learning to Drop: Robust Graph Neural Network via Topological Denoising","created":"2020-11-13","cited_by_count":127},{"id":"1905.10674","title":"Compositional Fairness Constraints for Graph Embeddings","created":"2019-05-25","cited_by_count":126},{"id":"2005.02131","title":"Stealing Links from Graph Neural Networks","created":"2020-05-05","cited_by_count":115},{"id":"2106.13423","title":"Federated Graph Classification over Non-IID Graphs","created":"2021-06-25","cited_by_count":114},{"id":"2006.11890","title":"Graph Backdoor","created":"2020-06-21","cited_by_count":112},{"id":"2011.09643","title":"Node Similarity Preserving Graph Convolutional Networks","created":"2020-11-19","cited_by_count":112},{"id":"1809.01093","title":"Adversarial Attacks on Node Embeddings via Graph Poisoning","created":"2018-09-04","cited_by_count":109},{"id":"2204.04879","title":"How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision","created":"2022-04-11","cited_by_count":102},{"id":"2110.14038","title":"Robustness of Graph Neural Networks at Scale","created":"2021-10-26","cited_by_count":100},{"id":"1906.01210","title":"Attributed Graph Clustering via Adaptive Graph Convolution","created":"2019-06-04","cited_by_count":96},{"id":"2112.00911","title":"ProtGNN: Towards Self-Explaining Graph Neural Networks","created":"2021-12-02","cited_by_count":96},{"id":"1902.08226","title":"Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure","created":"2019-02-20","cited_by_count":93},{"id":"2107.06641","title":"Trustworthy AI: A Computational Perspective","created":"2021-07-12","cited_by_count":93},{"id":"2108.05233","title":"EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks","created":"2021-08-11","cited_by_count":92},{"id":"2102.05429","title":"Node-Level Membership Inference Attacks Against Graph Neural Networks","created":"2021-02-10","cited_by_count":84},{"id":"2010.00906","title":"Quantifying Privacy Leakage in Graph Embedding","created":"2020-10-02","cited_by_count":82},{"id":"2101.06570","title":"Membership Inference Attack on Graph Neural Networks","created":"2021-01-17","cited_by_count":81},{"id":"1804.11238","title":"Privacy Preserving Machine Learning: Threats and Solutions","created":"2018-03-27","cited_by_count":79},{"id":"2111.15367","title":"A Review on Graph Neural Network Methods in Financial Applications","created":"2021-11-27","cited_by_count":78},{"id":"2006.05057","title":"Towards More Practical Adversarial Attacks on Graph Neural Networks","created":"2020-06-09","cited_by_count":76},{"id":"2108.12055","title":"Towards Self-Explainable Graph Neural Network","created":"2021-08-26","cited_by_count":75},{"id":"2106.04714","title":"NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs","created":"2021-06-08","cited_by_count":73},{"id":"1908.01297","title":"A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models","created":"2019-08-04","cited_by_count":68},{"id":"1412.7584","title":"Differential Privacy and Machine Learning: a Survey and Review","created":"2014-12-24","cited_by_count":67},{"id":"2105.13137","title":"Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future","created":"2021-05-27","cited_by_count":67},{"id":"2106.06663","title":"TDGIA:Effective Injection Attacks on Graph Neural Networks","created":"2021-06-12","cited_by_count":67},{"id":"1809.02797","title":"Fast Gradient Attack on Network Embedding","created":"2018-09-08","cited_by_count":65},{"id":"2006.05535","title":"Locally Private Graph Neural Networks","created":"2020-06-09","cited_by_count":64}],"next_cursor":"100"}