{"list":"other","total":119,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"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":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1806.07366","title":"Neural Ordinary Differential Equations","created":"2018-06-19","cited_by_count":3779},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"1611.01144","title":"Categorical Reparameterization with Gumbel-Softmax","created":"2016-11-03","cited_by_count":2739},{"id":"1903.02428","title":"Fast Graph Representation Learning with PyTorch Geometric","created":"2019-03-06","cited_by_count":2434},{"id":"1412.3555","title":"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling","created":"2014-12-11","cited_by_count":2367},{"id":"2005.00687","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","created":"2020-05-02","cited_by_count":2163},{"id":"1606.09375","title":"Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering","created":"2016-06-30","cited_by_count":2089},{"id":"1308.3432","title":"Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation","created":"2013-08-15","cited_by_count":2079},{"id":"1910.03193","title":"DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators","created":"2019-10-08","cited_by_count":2018},{"id":"1703.06103","title":"Modeling Relational Data with Graph Convolutional Networks","created":"2017-03-17","cited_by_count":1694},{"id":"1803.01271","title":"An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling","created":"2018-03-04","cited_by_count":1476},{"id":"1703.06114","title":"Deep Sets","created":"2017-03-10","cited_by_count":1421},{"id":"1806.01261","title":"Relational inductive biases, deep learning, and graph networks","created":"2018-06-04","cited_by_count":1360},{"id":"1409.1259","title":"On the Properties of Neural Machine Translation: Encoder-Decoder Approaches","created":"2014-09-03","cited_by_count":1304},{"id":"2104.13478","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","created":"2021-04-27","cited_by_count":1167},{"id":"1611.00712","title":"The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables","created":"2016-11-02","cited_by_count":1148},{"id":"1709.04875","title":"Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting","created":"2017-09-14","cited_by_count":1136},{"id":"1312.6203","title":"Spectral Networks and Locally Connected Networks on Graphs","created":"2013-12-21","cited_by_count":1102},{"id":"1704.04110","title":"DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks","created":"2017-04-13","cited_by_count":1013},{"id":"1906.00121","title":"Graph WaveNet for Deep Spatial-Temporal Graph Modeling","created":"2019-05-31","cited_by_count":829},{"id":"1907.10903","title":"DropEdge: Towards Deep Graph Convolutional Networks on Node Classification","created":"2019-07-25","cited_by_count":680},{"id":"2003.00982","title":"Benchmarking Graph Neural Networks","created":"2020-03-02","cited_by_count":680},{"id":"1907.04931","title":"GraphSAINT: Graph Sampling Based Inductive Learning Method","created":"2019-07-10","cited_by_count":618},{"id":"1907.00235","title":"Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting","created":"2019-06-29","cited_by_count":607},{"id":"2005.11650","title":"Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks","created":"2020-05-24","cited_by_count":566},{"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":"2007.02842","title":"Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting","created":"2020-07-06","cited_by_count":530},{"id":"2005.08926","title":"Neural Controlled Differential Equations for Irregular Time Series","created":"2020-05-18","cited_by_count":506},{"id":"1902.10191","title":"EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs","created":"2019-02-26","cited_by_count":447},{"id":"1911.08415","title":"GMAN: A Graph Multi-Attention Network for Traffic Prediction","created":"2019-11-11","cited_by_count":434},{"id":"1712.00468","title":"Graph Signal Processing: Overview, Challenges and Applications","created":"2017-12-01","cited_by_count":413},{"id":"1811.05320","title":"T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction","created":"2018-11-12","cited_by_count":401},{"id":"1806.02920","title":"GAIN: Missing Data Imputation using Generative Adversarial Nets","created":"2018-06-07","cited_by_count":352},{"id":"1711.07553","title":"Residual Gated Graph ConvNets","created":"2017-11-20","cited_by_count":346},{"id":"1802.04687","title":"Neural Relational Inference for Interacting Systems","created":"2018-02-13","cited_by_count":339},{"id":"2106.06947","title":"Graph Neural Network-Based Anomaly Detection in Multivariate Time Series","created":"2021-06-13","cited_by_count":301},{"id":"1805.10572","title":"BRITS: Bidirectional Recurrent Imputation for Time Series","created":"2018-05-27","cited_by_count":290},{"id":"2004.11198","title":"SIGN: Scalable Inception Graph Neural Networks","created":"2020-04-23","cited_by_count":284},{"id":"1906.10652","title":"Monte Carlo Gradient Estimation in Machine Learning","created":"2019-06-25","cited_by_count":283},{"id":"1612.07659","title":"Structured Sequence Modeling with Graph Convolutional Recurrent Networks","created":"2016-12-22","cited_by_count":275},{"id":"1905.11485","title":"Representation Learning for Dynamic Graphs: A Survey","created":"2019-05-27","cited_by_count":265},{"id":"2101.11174","title":"Graph Neural Network for Traffic Forecasting: A Survey","created":"2021-01-27","cited_by_count":261},{"id":"2106.10934","title":"GRAND: Graph Neural Diffusion","created":"2021-06-21","cited_by_count":258},{"id":"2303.10993","title":"A Survey on Oversmoothing in Graph Neural Networks","created":"2023-03-20","cited_by_count":247},{"id":"1903.11960","title":"Learning Discrete Structures for Graph Neural Networks","created":"2019-03-28","cited_by_count":231},{"id":"1711.11053","title":"A Multi-Horizon Quantile Recurrent Forecaster","created":"2017-11-29","cited_by_count":211},{"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":"2106.12931","title":"Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting","created":"2021-06-24","cited_by_count":210},{"id":"2101.06861","title":"Discrete Graph Structure Learning for Forecasting Multiple Time Series","created":"2021-01-18","cited_by_count":171},{"id":"2205.11172","title":"How Powerful are Spectral Graph Neural Networks","created":"2022-05-23","cited_by_count":169},{"id":"1803.07294","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","created":"2018-03-20","cited_by_count":165},{"id":"2208.05233","title":"Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting","created":"2022-08-10","cited_by_count":151},{"id":"2307.03759","title":"A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection","created":"2023-07-07","cited_by_count":145},{"id":"2303.14483","title":"Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey","created":"2023-03-25","cited_by_count":140},{"id":"2307.01026","title":"Temporal Graph Benchmark for Machine Learning on Temporal Graphs","created":"2023-07-03","cited_by_count":138},{"id":"2112.03558","title":"Graph Neural Controlled Differential Equations for Traffic Forecasting","created":"2021-12-07","cited_by_count":133},{"id":"2005.07496","title":"Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey","created":"2020-05-13","cited_by_count":124},{"id":"2006.03548","title":"Graphon Neural Networks and the Transferability of Graph Neural Networks","created":"2020-06-05","cited_by_count":118},{"id":"2104.07788","title":"PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models","created":"2021-04-15","cited_by_count":118},{"id":"2306.08259","title":"LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting","created":"2023-06-14","cited_by_count":118},{"id":"2004.10240","title":"Deep Learning for Time Series Forecasting: Tutorial and Literature Survey","created":"2020-04-21","cited_by_count":117},{"id":"2110.05357","title":"Graph-Guided Network for Irregularly Sampled Multivariate Time Series","created":"2021-10-11","cited_by_count":111},{"id":"1912.12693","title":"A Gentle Introduction to Deep Learning for Graphs","created":"2019-12-29","cited_by_count":108},{"id":"2106.01798","title":"Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions","created":"2021-06-03","cited_by_count":98},{"id":"2108.00298","title":"Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks","created":"2021-07-31","cited_by_count":95},{"id":"2006.07527","title":"Inductive Graph Neural Networks for Spatiotemporal Kriging","created":"2020-06-13","cited_by_count":91},{"id":"2002.04999","title":"Differentiable Graph Module (DGM) for Graph Convolutional Networks","created":"2020-02-11","cited_by_count":86},{"id":"1606.04130","title":"Modeling Missing Data in Clinical Time Series with RNNs","created":"2016-06-13","cited_by_count":80},{"id":"2202.08408","title":"Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs","created":"2022-02-17","cited_by_count":72},{"id":"2008.00444","title":"Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality","created":"2020-08-02","cited_by_count":70},{"id":"2110.05292","title":"Understanding Pooling in Graph Neural Networks","created":"2021-10-11","cited_by_count":69},{"id":"2006.08063","title":"Gradient Estimation with Stochastic Softmax Tricks","created":"2020-06-15","cited_by_count":67},{"id":"2302.09746","title":"PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation","created":"2023-02-20","cited_by_count":66},{"id":"2007.03113","title":"Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks","created":"2020-07-06","cited_by_count":64},{"id":"2205.13479","title":"Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations","created":"2022-05-26","cited_by_count":64},{"id":"2302.01018","title":"Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities","created":"2023-02-02","cited_by_count":61},{"id":"2101.01229","title":"A Survey on Embedding Dynamic Graphs","created":"2021-01-04","cited_by_count":60},{"id":"2209.06520","title":"Scalable Spatiotemporal Graph Neural Networks","created":"2022-09-14","cited_by_count":52},{"id":"2105.11982","title":"Quantifying Uncertainty in Deep Spatiotemporal Forecasting","created":"2021-05-25","cited_by_count":48},{"id":"2212.03523","title":"Criteria for Classifying Forecasting Methods","created":"2022-12-07","cited_by_count":48},{"id":"2012.00168","title":"A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series","created":"2020-11-30","cited_by_count":42},{"id":"2303.12211","title":"Graph Signal Processing: History, Development, Impact, and Outlook","created":"2023-03-21","cited_by_count":39},{"id":"1803.07870","title":"Reservoir computing approaches for representation and classification of multivariate time series","created":"2018-03-21","cited_by_count":38},{"id":"2005.11691","title":"How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey","created":"2020-05-24","cited_by_count":38},{"id":"2302.04071","title":"Taming Local Effects in Graph-based Spatiotemporal Forecasting","created":"2023-02-08","cited_by_count":37},{"id":"2304.01575","title":"The expressive power of pooling in Graph Neural Networks","created":"2023-04-04","cited_by_count":35},{"id":"2210.01941","title":"SIMPLE: A Gradient Estimator for $k$-Subset Sampling","created":"2022-10-04","cited_by_count":34},{"id":"2108.12238","title":"Group-Aware Graph Neural Network for Nationwide City Air Quality Forecasting","created":"2021-08-27","cited_by_count":32},{"id":"2305.18612","title":"Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders","created":"2023-05-29","cited_by_count":32},{"id":"2007.15531","title":"FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting","created":"2020-07-30","cited_by_count":30},{"id":"1810.08581","title":"Forecasting Time Series with VARMA Recursions on Graphs","created":"2018-10-19","cited_by_count":27},{"id":"1909.10660","title":"Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis","created":"2019-09-24","cited_by_count":27},{"id":"2302.02738","title":"INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging","created":"2023-02-06","cited_by_count":27},{"id":"2102.12612","title":"Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series","created":"2021-02-25","cited_by_count":24}],"next_cursor":"100"}