{"list":"other","total":67,"rows":[{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"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":"1612.00796","title":"Overcoming catastrophic forgetting in neural networks","created":"2016-12-02","cited_by_count":4787},{"id":"1606.09282","title":"Learning without Forgetting","created":"2016-06-29","cited_by_count":2496},{"id":"1903.02428","title":"Fast Graph Representation Learning with PyTorch Geometric","created":"2019-03-06","cited_by_count":2434},{"id":"1611.07725","title":"iCaRL: Incremental Classifier and Representation Learning","created":"2016-11-23","cited_by_count":2333},{"id":"2005.00687","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","created":"2020-05-02","cited_by_count":2163},{"id":"1706.08840","title":"Gradient Episodic Memory for Continual Learning","created":"2017-06-26","cited_by_count":1898},{"id":"2010.13902","title":"Graph Contrastive Learning with Augmentations","created":"2020-10-22","cited_by_count":1208},{"id":"1705.08690","title":"Continual Learning with Deep Generative Replay","created":"2017-05-24","cited_by_count":1158},{"id":"1809.10341","title":"Deep Graph Infomax","created":"2018-09-27","cited_by_count":1106},{"id":"1711.09601","title":"Memory Aware Synapses: Learning what (not) to forget","created":"2017-11-27","cited_by_count":1105},{"id":"1811.11682","title":"Experience Replay for Continual Learning","created":"2018-11-28","cited_by_count":1095},{"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":"1711.05769","title":"PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning","created":"2017-11-15","cited_by_count":898},{"id":"2002.05287","title":"Geom-GCN: Geometric Graph Convolutional Networks","created":"2020-02-13","cited_by_count":826},{"id":"1610.02415","title":"Automatic chemical design using a data-driven continuous representation of molecules","created":"2016-10-07","cited_by_count":765},{"id":"1801.10112","title":"Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence","created":"2018-01-30","cited_by_count":742},{"id":"1312.6211","title":"An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks","created":"2013-12-21","cited_by_count":722},{"id":"1506.04757","title":"Image-based Recommendations on Styles and Substitutes","created":"2015-06-15","cited_by_count":706},{"id":"1801.01423","title":"Overcoming catastrophic forgetting with hard attention to the task","created":"2018-01-04","cited_by_count":681},{"id":"2003.00982","title":"Benchmarking Graph Neural Networks","created":"2020-03-02","cited_by_count":680},{"id":"1905.08108","title":"Neural Graph Collaborative Filtering","created":"2019-05-20","cited_by_count":661},{"id":"1903.08671","title":"Gradient based sample selection for online continual learning","created":"2019-03-20","cited_by_count":571},{"id":"1905.07953","title":"Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks","created":"2019-05-20","cited_by_count":541},{"id":"2006.10637","title":"Temporal Graph Networks for Deep Learning on Dynamic Graphs","created":"2020-06-18","cited_by_count":530},{"id":"1602.01585","title":"Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering","created":"2016-02-04","cited_by_count":520},{"id":"2002.07962","title":"Inductive Representation Learning on Temporal Graphs","created":"2020-02-19","cited_by_count":462},{"id":"1908.01000","title":"InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization","created":"2019-07-31","cited_by_count":441},{"id":"1611.06194","title":"Expert Gate: Lifelong Learning with a Network of Experts","created":"2016-11-18","cited_by_count":372},{"id":"1701.08734","title":"PathNet: Evolution Channels Gradient Descent in Super Neural Networks","created":"2017-01-30","cited_by_count":364},{"id":"1707.03815","title":"Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking","created":"2017-07-12","cited_by_count":335},{"id":"1705.03550","title":"CORe50: a New Dataset and Benchmark for Continuous Object Recognition","created":"2017-05-09","cited_by_count":289},{"id":"1809.01286","title":"FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media","created":"2018-09-05","cited_by_count":285},{"id":"2007.02901","title":"Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks","created":"2020-07-06","cited_by_count":260},{"id":"1912.09893","title":"A Fair Comparison of Graph Neural Networks for Graph Classification","created":"2019-12-20","cited_by_count":244},{"id":"1909.01315","title":"Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks","created":"2019-09-03","cited_by_count":242},{"id":"1802.10269","title":"Selective Experience Replay for Lifelong Learning","created":"2018-02-28","cited_by_count":241},{"id":"1810.12488","title":"Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines","created":"2018-10-30","cited_by_count":228},{"id":"1908.02591","title":"Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics","created":"2019-07-31","cited_by_count":164},{"id":"1707.04638","title":"Predicting multicellular function through multi-layer tissue networks","created":"2017-07-14","cited_by_count":138},{"id":"2208.07239","title":"ROLAND: Graph Learning Framework for Dynamic Graphs","created":"2022-08-15","cited_by_count":132},{"id":"2104.00405","title":"Avalanche: an End-to-End Library for Continual Learning","created":"2021-04-01","cited_by_count":124},{"id":"1612.09259","title":"Motifs in Temporal Networks","created":"2016-12-29","cited_by_count":101},{"id":"2012.06002","title":"Overcoming Catastrophic Forgetting in Graph Neural Networks","created":"2020-12-10","cited_by_count":95},{"id":"2201.06289","title":"The CLEAR Benchmark: Continual LEArning on Real-World Imagery","created":"2022-01-17","cited_by_count":93},{"id":"1506.08839","title":"Inferring Networks of Substitutable and Complementary Products","created":"2015-06-29","cited_by_count":92},{"id":"2112.03806","title":"OOD-GNN: Out-of-Distribution Generalized Graph Neural Network","created":"2021-12-07","cited_by_count":78},{"id":"1510.07025","title":"Modeling User Exposure in Recommendation","created":"2015-10-23","cited_by_count":69},{"id":"2009.10951","title":"Streaming Graph Neural Networks via Continual Learning","created":"2020-09-23","cited_by_count":64},{"id":"1802.03997","title":"GEMSEC: Graph Embedding with Self Clustering","created":"2018-02-12","cited_by_count":61},{"id":"2101.03091","title":"Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings","created":"2021-01-08","cited_by_count":49},{"id":"2006.06648","title":"Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction","created":"2020-06-11","cited_by_count":47},{"id":"2007.03316","title":"Graph Neural Networks with Continual Learning for Fake News Detection from Social Media","created":"2020-07-07","cited_by_count":43},{"id":"2106.14232","title":"DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science","created":"2021-06-27","cited_by_count":38},{"id":"2101.05850","title":"Continual Learning of Knowledge Graph Embeddings","created":"2021-01-14","cited_by_count":35},{"id":"2009.00647","title":"Lifelong Graph Learning","created":"2020-09-01","cited_by_count":31},{"id":"2202.10688","title":"Graph Lifelong Learning: A Survey","created":"2022-02-22","cited_by_count":30},{"id":"2209.01556","title":"Reinforced Continual Learning for Graphs","created":"2022-09-04","cited_by_count":24},{"id":"2006.14422","title":"Lifelong Learning of Graph Neural Networks for Open-World Node Classification","created":"2020-06-25","cited_by_count":22},{"id":"2101.00926","title":"CLeaR: An Adaptive Continual Learning Framework for Regression Tasks","created":"2021-01-04","cited_by_count":22},{"id":"2110.04514","title":"Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach","created":"2021-10-09","cited_by_count":20},{"id":"2103.11750","title":"Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification","created":"2021-03-22","cited_by_count":13},{"id":"2106.06210","title":"Learning to Pool in Graph Neural Networks for Extrapolation","created":"2021-06-11","cited_by_count":4}],"next_cursor":null}