{"list":"other","total":58,"rows":[{"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":"1810.00826","title":"How Powerful are Graph Neural Networks?","created":"2018-10-01","cited_by_count":4393},{"id":"1907.05600","title":"Generative Modeling by Estimating Gradients of the Data Distribution","created":"2019-07-12","cited_by_count":4101},{"id":"1312.6114","title":"Auto-Encoding Variational Bayes","created":"2013-12-20","cited_by_count":4053},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"1505.05770","title":"Variational Inference with Normalizing Flows","created":"2015-05-21","cited_by_count":2170},{"id":"1605.08803","title":"Density estimation using Real NVP","created":"2016-05-27","cited_by_count":2042},{"id":"1703.06103","title":"Modeling Relational Data with Graph Convolutional Networks","created":"2017-03-17","cited_by_count":1694},{"id":"1802.05957","title":"Spectral Normalization for Generative Adversarial Networks","created":"2018-02-16","cited_by_count":1636},{"id":"1807.03039","title":"Glow: Generative Flow with Invertible 1x1 Convolutions","created":"2018-07-09","cited_by_count":1545},{"id":"1902.07153","title":"Simplifying Graph Convolutional Networks","created":"2019-02-19","cited_by_count":1477},{"id":"2010.13902","title":"Graph Contrastive Learning with Augmentations","created":"2020-10-22","cited_by_count":1208},{"id":"1410.8516","title":"NICE: Non-linear Independent Components Estimation","created":"2014-10-30","cited_by_count":1058},{"id":"1703.00564","title":"MoleculeNet: A Benchmark for Molecular Machine Learning","created":"2017-03-02","cited_by_count":1051},{"id":"1810.05997","title":"Predict then Propagate: Graph Neural Networks meet Personalized PageRank","created":"2018-10-14","cited_by_count":993},{"id":"1806.03536","title":"Representation Learning on Graphs with Jumping Knowledge Networks","created":"2018-06-09","cited_by_count":877},{"id":"2007.02133","title":"Simple and Deep Graph Convolutional Networks","created":"2020-07-04","cited_by_count":860},{"id":"1802.09691","title":"Link Prediction Based on Graph Neural Networks","created":"2018-02-27","cited_by_count":832},{"id":"1806.08804","title":"Hierarchical Graph Representation Learning with Differentiable Pooling","created":"2018-06-22","cited_by_count":787},{"id":"1610.02415","title":"Automatic chemical design using a data-driven continuous representation of molecules","created":"2016-10-07","cited_by_count":765},{"id":"1802.04364","title":"Junction Tree Variational Autoencoder for Molecular Graph Generation","created":"2018-02-12","cited_by_count":652},{"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":"1805.11973","title":"MolGAN: An implicit generative model for small molecular graphs","created":"2018-05-30","cited_by_count":393},{"id":"1802.03480","title":"GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders","created":"2018-02-09","cited_by_count":383},{"id":"1904.01561","title":"Analyzing Learned Molecular Representations for Property Prediction","created":"2019-04-02","cited_by_count":375},{"id":"2005.10203","title":"Graph Structure Learning for Robust Graph Neural Networks","created":"2020-05-20","cited_by_count":371},{"id":"1806.02473","title":"Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation","created":"2018-06-07","cited_by_count":364},{"id":"1812.09902","title":"Invariant and Equivariant Graph Networks","created":"2018-12-24","cited_by_count":346},{"id":"2001.09382","title":"GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation","created":"2020-01-26","cited_by_count":305},{"id":"2007.09296","title":"Towards Deeper Graph Neural Networks","created":"2020-07-18","cited_by_count":296},{"id":"1703.01925","title":"Grammar Variational Autoencoder","created":"2017-03-06","cited_by_count":285},{"id":"2101.03288","title":"How to Train Your Energy-Based Models","created":"2021-01-09","cited_by_count":274},{"id":"2003.00638","title":"Permutation Invariant Graph Generation via Score-Based Generative Modeling","created":"2020-03-02","cited_by_count":270},{"id":"1803.03324","title":"Learning Deep Generative Models of Graphs","created":"2018-03-08","cited_by_count":229},{"id":"2002.03230","title":"Hierarchical Generation of Molecular Graphs using Structural Motifs","created":"2020-02-08","cited_by_count":208},{"id":"1805.09076","title":"Constrained Graph Variational Autoencoders for Molecule Design","created":"2018-05-23","cited_by_count":185},{"id":"2006.10137","title":"MoFlow: An Invertible Flow Model for Generating Molecular Graphs","created":"2020-06-17","cited_by_count":185},{"id":"1602.03264","title":"A Theory of Generative ConvNet","created":"2016-02-10","cited_by_count":174},{"id":"2012.01316","title":"Improved Contrastive Divergence Training of Energy Based Models","created":"2020-12-02","cited_by_count":130},{"id":"1905.11600","title":"GraphNVP: An Invertible Flow Model for Generating Molecular Graphs","created":"2019-05-28","cited_by_count":116},{"id":"1808.03965","title":"Large-Scale Learnable Graph Convolutional Networks","created":"2018-08-12","cited_by_count":113},{"id":"1802.08786","title":"Syntax-Directed Variational Autoencoder for Structured Data","created":"2018-02-24","cited_by_count":103},{"id":"1904.13107","title":"Graph Convolutional Networks with EigenPooling","created":"2019-04-30","cited_by_count":94},{"id":"2005.14612","title":"Non-Local Graph Neural Networks","created":"2020-05-29","cited_by_count":91},{"id":"2010.10046","title":"Line Graph Neural Networks for Link Prediction","created":"2020-10-20","cited_by_count":89},{"id":"1905.13372","title":"MolecularRNN: Generating realistic molecular graphs with optimized properties","created":"2019-05-31","cited_by_count":82},{"id":"1809.02630","title":"Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders","created":"2018-09-07","cited_by_count":74},{"id":"2004.01301","title":"Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification","created":"2020-04-02","cited_by_count":57},{"id":"1804.00586","title":"Learning Descriptor Networks for 3D Shape Synthesis and Analysis","created":"2018-04-02","cited_by_count":56},{"id":"2012.01981","title":"Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery","created":"2020-12-02","cited_by_count":46},{"id":"2010.09834","title":"Topology-Aware Graph Pooling Networks","created":"2020-10-19","cited_by_count":44},{"id":"2006.09790","title":"Categorical Normalizing Flows via Continuous Transformations","created":"2020-06-17","cited_by_count":37},{"id":"1906.03412","title":"A Two-Step Graph Convolutional Decoder for Molecule Generation","created":"2019-06-08","cited_by_count":25},{"id":"1909.13521","title":"Graph Residual Flow for Molecular Graph Generation","created":"2019-09-30","cited_by_count":23},{"id":"1911.01731","title":"GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction","created":"2019-11-05","cited_by_count":7}],"next_cursor":null}