{"list":"other","total":74,"rows":[{"id":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12061},{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11405},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6325},{"id":"1810.00826","title":"How Powerful are Graph Neural Networks?","created":"2018-10-01","cited_by_count":4410},{"id":"2106.01946","title":"Convex optimization","created":"2021-06-03","cited_by_count":2518},{"id":"2005.00687","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","created":"2020-05-02","cited_by_count":2165},{"id":"1403.6652","title":"DeepWalk: Online Learning of Social Representations","created":"2014-03-26","cited_by_count":1547},{"id":"1611.07308","title":"Variational Graph Auto-Encoders","created":"2016-11-21","cited_by_count":1390},{"id":"2010.13902","title":"Graph Contrastive Learning with Augmentations","created":"2020-10-22","cited_by_count":1209},{"id":"1809.10341","title":"Deep Graph Infomax","created":"2018-09-27","cited_by_count":1107},{"id":"1711.10925","title":"Deep Image Prior","created":"2017-11-29","cited_by_count":1100},{"id":"2105.04906","title":"VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning","created":"2021-05-11","cited_by_count":1068},{"id":"1703.00564","title":"MoleculeNet: A Benchmark for Molecular Machine Learning","created":"2017-03-02","cited_by_count":1052},{"id":"1905.12265","title":"Strategies for Pre-training Graph Neural Networks","created":"2019-05-29","cited_by_count":976},{"id":"1612.00410","title":"Deep Variational Information Bottleneck","created":"2016-12-01","cited_by_count":934},{"id":"2005.10243","title":"What Makes for Good Views for Contrastive Learning?","created":"2020-05-20","cited_by_count":846},{"id":"1802.09691","title":"Link Prediction Based on Graph Neural Networks","created":"2018-02-27","cited_by_count":832},{"id":"2007.08663","title":"TUDataset: A collection of benchmark datasets for learning with graphs","created":"2020-07-16","cited_by_count":734},{"id":"2006.08218","title":"Self-supervised Learning: Generative or Contrastive","created":"2020-06-15","cited_by_count":694},{"id":"1801.04062","title":"MINE: Mutual Information Neural Estimation","created":"2018-01-12","cited_by_count":685},{"id":"1802.04364","title":"Junction Tree Variational Autoencoder for Molecular Graph Generation","created":"2018-02-12","cited_by_count":652},{"id":"2006.05582","title":"Contrastive Multi-View Representation Learning on Graphs","created":"2020-06-10","cited_by_count":652},{"id":"1503.03578","title":"LINE: Large-scale Information Network Embedding","created":"2015-03-12","cited_by_count":582},{"id":"2010.14945","title":"Graph Contrastive Learning with Adaptive Augmentation","created":"2020-10-27","cited_by_count":519},{"id":"2006.09963","title":"GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training","created":"2020-06-17","cited_by_count":463},{"id":"1908.01000","title":"InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization","created":"2019-07-31","cited_by_count":442},{"id":"1802.08773","title":"GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models","created":"2018-02-24","cited_by_count":413},{"id":"1703.03208","title":"Compressed Sensing using Generative Models","created":"2017-03-09","cited_by_count":339},{"id":"2001.09382","title":"GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation","created":"2020-01-26","cited_by_count":306},{"id":"2006.15437","title":"GPT-GNN: Generative Pre-Training of Graph Neural Networks","created":"2020-06-27","cited_by_count":303},{"id":"2103.00111","title":"Graph Self-Supervised Learning: A Survey","created":"2021-02-27","cited_by_count":289},{"id":"2106.07594","title":"Graph Contrastive Learning Automated","created":"2021-06-10","cited_by_count":287},{"id":"1802.07687","title":"Stochastic Video Generation with a Learned Prior","created":"2018-02-21","cited_by_count":245},{"id":"1707.05005","title":"graph2vec: Learning Distributed Representations of Graphs","created":"2017-07-17","cited_by_count":231},{"id":"2002.01169","title":"Graph Representation Learning via Graphical Mutual Information Maximization","created":"2020-02-04","cited_by_count":217},{"id":"2006.06830","title":"Data Augmentation for Graph Neural Networks","created":"2020-06-11","cited_by_count":203},{"id":"2106.05819","title":"Adversarial Graph Augmentation to Improve Graph Contrastive Learning","created":"2021-06-10","cited_by_count":200},{"id":"2102.10757","title":"Self-Supervised Learning of Graph Neural Networks: A Unified Review","created":"2021-02-22","cited_by_count":184},{"id":"2102.01189","title":"GraphDF: A Discrete Flow Model for Molecular Graph Generation","created":"2021-02-01","cited_by_count":180},{"id":"1911.07323","title":"Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks","created":"2019-11-17","cited_by_count":177},{"id":"2010.12811","title":"Graph Information Bottleneck","created":"2020-10-24","cited_by_count":168},{"id":"1803.00816","title":"NetGAN: Generating Graphs via Random Walks","created":"2018-03-02","cited_by_count":131},{"id":"1711.08267","title":"GraphGAN: Graph Representation Learning with Generative Adversarial Nets","created":"2017-11-22","cited_by_count":121},{"id":"2006.09136","title":"When Does Self-Supervision Help Graph Convolutional Networks?","created":"2020-06-16","cited_by_count":121},{"id":"2010.05563","title":"Graph Information Bottleneck for Subgraph Recognition","created":"2020-10-12","cited_by_count":120},{"id":"1902.11038","title":"Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels","created":"2019-02-28","cited_by_count":111},{"id":"2006.10141","title":"Self-supervised Learning on Graphs: Deep Insights and New Direction","created":"2020-06-17","cited_by_count":108},{"id":"2110.15438","title":"InfoGCL: Information-Aware Graph Contrastive Learning","created":"2021-10-28","cited_by_count":105},{"id":"2105.06868","title":"Priors in Bayesian Deep Learning: A Review","created":"2021-05-14","cited_by_count":102},{"id":"2101.06448","title":"Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation","created":"2021-01-16","cited_by_count":101},{"id":"1911.06750","title":"Unsupervised Attributed Multiplex Network Embedding","created":"2019-11-15","cited_by_count":95},{"id":"2102.00546","title":"GraphEBM: Molecular Graph Generation with Energy-Based Models","created":"2021-01-31","cited_by_count":86},{"id":"1802.08406","title":"Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable Guarantees","created":"2018-02-23","cited_by_count":58},{"id":"2106.05470","title":"Automated Self-Supervised Learning for Graphs","created":"2021-06-10","cited_by_count":50},{"id":"2003.13606","title":"L$^2$-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks","created":"2020-03-30","cited_by_count":48},{"id":"1911.08538","title":"Heterogeneous Deep Graph Infomax","created":"2019-11-19","cited_by_count":42},{"id":"2012.07064","title":"Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation","created":"2020-12-13","cited_by_count":42},{"id":"1905.04579","title":"Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification","created":"2019-05-11","cited_by_count":39},{"id":"2003.01604","title":"Self-Supervised Graph Representation Learning via Global Context Prediction","created":"2020-03-03","cited_by_count":38},{"id":"2007.08294","title":"Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs","created":"2020-07-16","cited_by_count":38},{"id":"1705.07576","title":"Global Guarantees for Enforcing Deep Generative Priors by Empirical Risk","created":"2017-05-22","cited_by_count":37},{"id":"2106.09645","title":"Prototypical Graph Contrastive Learning","created":"2021-06-17","cited_by_count":37},{"id":"2104.07917","title":"Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks","created":"2021-04-16","cited_by_count":31},{"id":"1909.11715","title":"GraphMix: Improved Training of GNNs for Semi-Supervised Learning","created":"2019-09-25","cited_by_count":23},{"id":"2010.12609","title":"Iterative Graph Self-Distillation","created":"2020-10-23","cited_by_count":21},{"id":"2011.07267","title":"Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks","created":"2020-11-14","cited_by_count":21},{"id":"1802.01284","title":"Task-Aware Compressed Sensing with Generative Adversarial Networks","created":"2018-02-05","cited_by_count":17},{"id":"1706.06549","title":"Inference in Deep Networks in High Dimensions","created":"2017-06-20","cited_by_count":13},{"id":"1803.04477","title":"Correction by Projection: Denoising Images with Generative Adversarial Networks","created":"2018-03-12","cited_by_count":11},{"id":"2103.00771","title":"Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning","created":"2021-03-01","cited_by_count":6},{"id":"2104.13014","title":"Node Embedding using Mutual Information and Self-Supervision based Bi-level Aggregation","created":"2021-04-27","cited_by_count":5},{"id":"2107.09787","title":"Group Contrastive Self-Supervised Learning on Graphs","created":"2021-07-20","cited_by_count":5},{"id":"2007.04777","title":"Self-supervised edge features for improved Graph Neural Network training","created":"2020-06-23","cited_by_count":3},{"id":"2012.00651","title":"Cross-Modality Protein Embedding for Compound-Protein Affinity and Contact Prediction","created":"2020-11-14","cited_by_count":3}],"next_cursor":null}