{"list":"other","total":53,"rows":[{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"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":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"1706.06083","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","created":"2017-06-19","cited_by_count":6415},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"1903.02428","title":"Fast Graph Representation Learning with PyTorch Geometric","created":"2019-03-06","cited_by_count":2434},{"id":"1806.09055","title":"DARTS: Differentiable Architecture Search","created":"2018-06-24","cited_by_count":1798},{"id":"1403.6652","title":"DeepWalk: Online Learning of Social Representations","created":"2014-03-26","cited_by_count":1547},{"id":"1806.01973","title":"Graph Convolutional Neural Networks for Web-Scale Recommender Systems","created":"2018-06-06","cited_by_count":1138},{"id":"1902.07243","title":"Graph Neural Networks for Social Recommendation","created":"2019-02-19","cited_by_count":714},{"id":"1909.13021","title":"Multi-scale Attributed Node Embedding","created":"2019-09-28","cited_by_count":548},{"id":"1805.07984","title":"Adversarial Attacks on Neural Networks for Graph Data","created":"2018-05-21","cited_by_count":397},{"id":"2005.10203","title":"Graph Structure Learning for Robust Graph Neural Networks","created":"2020-05-20","cited_by_count":371},{"id":"1806.02371","title":"Adversarial Attack on Graph Structured Data","created":"2018-06-06","cited_by_count":301},{"id":"2106.06947","title":"Graph Neural Network-Based Anomaly Detection in Multivariate Time Series","created":"2021-06-13","cited_by_count":301},{"id":"1909.12223","title":"PairNorm: Tackling Oversmoothing in GNNs","created":"2019-09-26","cited_by_count":297},{"id":"1902.08412","title":"Adversarial Attacks on Graph Neural Networks via Meta Learning","created":"2019-02-22","cited_by_count":261},{"id":"1903.06059","title":"Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement","created":"2019-03-14","cited_by_count":211},{"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":"1812.10528","title":"Adversarial Attack and Defense on Graph Data: A Survey","created":"2018-12-26","cited_by_count":167},{"id":"2302.02941","title":"On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology","created":"2023-02-06","cited_by_count":166},{"id":"2202.05146","title":"EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction","created":"2022-02-07","cited_by_count":144},{"id":"2011.07057","title":"Learning to Drop: Robust Graph Neural Network via Topological Denoising","created":"2020-11-13","cited_by_count":127},{"id":"1809.01093","title":"Adversarial Attacks on Node Embeddings via Graph Poisoning","created":"2018-09-04","cited_by_count":109},{"id":"2110.14038","title":"Robustness of Graph Neural Networks at Scale","created":"2021-10-26","cited_by_count":100},{"id":"2108.01099","title":"Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data","created":"2021-08-02","cited_by_count":87},{"id":"2002.04999","title":"Differentiable Graph Module (DGM) for Graph Convolutional Networks","created":"2020-02-11","cited_by_count":86},{"id":"2005.06149","title":"DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses","created":"2020-05-13","cited_by_count":78},{"id":"1608.00375","title":"Hiding Individuals and Communities in a Social Network","created":"2016-08-01","cited_by_count":77},{"id":"2006.05057","title":"Towards More Practical Adversarial Attacks on Graph Neural Networks","created":"2020-06-09","cited_by_count":76},{"id":"2103.03036","title":"A Survey on Graph Structure Learning: Progress and Opportunities","created":"2021-03-04","cited_by_count":71},{"id":"1901.10517","title":"Reparameterizable Subset Sampling via Continuous Relaxations","created":"2019-01-29","cited_by_count":70},{"id":"1908.01297","title":"A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models","created":"2019-08-04","cited_by_count":68},{"id":"2106.06663","title":"TDGIA:Effective Injection Attacks on Graph Neural Networks","created":"2021-06-12","cited_by_count":67},{"id":"2105.06339","title":"Graph Learning based Recommender Systems: A Review","created":"2021-05-13","cited_by_count":59},{"id":"1908.07558","title":"Transferring Robustness for Graph Neural Network Against Poisoning Attacks","created":"2019-08-20","cited_by_count":58},{"id":"2008.10715","title":"Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation","created":"2020-08-24","cited_by_count":57},{"id":"1903.00553","title":"Attacking Graph-based Classification via Manipulating the Graph Structure","created":"2019-03-01","cited_by_count":53},{"id":"1902.08661","title":"Learning protein sequence embeddings using information from structure","created":"2019-02-22","cited_by_count":51},{"id":"2303.01263","title":"Unnoticeable Backdoor Attacks on Graph Neural Networks","created":"2023-02-11","cited_by_count":45},{"id":"1910.14147","title":"A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning","created":"2019-10-30","cited_by_count":31},{"id":"2107.03813","title":"Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation","created":"2021-07-08","cited_by_count":22},{"id":"2106.10785","title":"Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem","created":"2021-06-21","cited_by_count":20},{"id":"2304.00010","title":"Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias","created":"2023-03-29","cited_by_count":16},{"id":"2007.09647","title":"Adversarial Immunization for Certifiable Robustness on Graphs","created":"2020-07-19","cited_by_count":14},{"id":"2112.13705","title":"Graph Collaborative Reasoning","created":"2021-12-27","cited_by_count":14},{"id":"2005.11560","title":"Adversarial Attack on Hierarchical Graph Pooling Neural Networks","created":"2020-05-23","cited_by_count":13},{"id":"2208.05514","title":"Are Gradients on Graph Structure Reliable in Gray-box Attacks?","created":"2022-08-07","cited_by_count":10}],"next_cursor":null}