{"list":"other","total":120,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"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":"1409.0473","title":"Neural Machine Translation by Jointly Learning to Align and Translate","created":"2014-09-01","cited_by_count":4548},{"id":"1810.00826","title":"How Powerful are Graph Neural Networks?","created":"2018-10-01","cited_by_count":4393},{"id":"1301.3781","title":"Efficient Estimation of Word Representations in Vector Space","created":"2013-01-16","cited_by_count":4375},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"1409.3215","title":"Sequence to Sequence Learning with Neural Networks","created":"2014-09-10","cited_by_count":2986},{"id":"1901.00596","title":"A Comprehensive Survey on Graph Neural Networks","created":"2019-01-03","cited_by_count":2919},{"id":"1412.3555","title":"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling","created":"2014-12-11","cited_by_count":2367},{"id":"1606.09375","title":"Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering","created":"2016-06-30","cited_by_count":2089},{"id":"1607.00653","title":"node2vec: Scalable Feature Learning for Networks","created":"2016-07-03","cited_by_count":1918},{"id":"1609.03499","title":"WaveNet: A Generative Model for Raw Audio","created":"2016-09-12","cited_by_count":1722},{"id":"1703.06103","title":"Modeling Relational Data with Graph Convolutional Networks","created":"2017-03-17","cited_by_count":1694},{"id":"1812.08434","title":"Graph Neural Networks: A Review of Methods and Applications","created":"2018-12-20","cited_by_count":1576},{"id":"1403.6652","title":"DeepWalk: Online Learning of Social Representations","created":"2014-03-26","cited_by_count":1547},{"id":"2105.01601","title":"MLP-Mixer: An all-MLP Architecture for Vision","created":"2021-05-04","cited_by_count":1446},{"id":"1611.07308","title":"Variational Graph Auto-Encoders","created":"2016-11-21","cited_by_count":1382},{"id":"1801.07455","title":"Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition","created":"2018-01-23","cited_by_count":1209},{"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":"1511.07122","title":"Multi-Scale Context Aggregation by Dilated Convolutions","created":"2015-11-23","cited_by_count":1133},{"id":"1803.02155","title":"Self-Attention with Relative Position Representations","created":"2018-03-06","cited_by_count":1120},{"id":"1312.6203","title":"Spectral Networks and Locally Connected Networks on Graphs","created":"2013-12-21","cited_by_count":1102},{"id":"1611.08097","title":"Geometric deep learning: going beyond Euclidean data","created":"2016-11-24","cited_by_count":1048},{"id":"1906.00121","title":"Graph WaveNet for Deep Spatial-Temporal Graph Modeling","created":"2019-05-31","cited_by_count":829},{"id":"1511.05493","title":"Gated Graph Sequence Neural Networks","created":"2015-11-17","cited_by_count":796},{"id":"1806.08804","title":"Hierarchical Graph Representation Learning with Differentiable Pooling","created":"2018-06-22","cited_by_count":787},{"id":"1903.07293","title":"Heterogeneous Graph Attention Network","created":"2019-03-18","cited_by_count":734},{"id":"1612.08083","title":"Language Modeling with Gated Convolutional Networks","created":"2016-12-23","cited_by_count":702},{"id":"1604.02808","title":"NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis","created":"2016-04-11","cited_by_count":686},{"id":"2003.00982","title":"Benchmarking Graph Neural Networks","created":"2020-03-02","cited_by_count":680},{"id":"1303.5778","title":"Speech Recognition with Deep Recurrent Neural Networks","created":"2013-03-22","cited_by_count":669},{"id":"1905.08108","title":"Neural Graph Collaborative Filtering","created":"2019-05-20","cited_by_count":661},{"id":"2006.05205","title":"On the Bottleneck of Graph Neural Networks and its Practical Implications","created":"2020-06-09","cited_by_count":631},{"id":"1503.03578","title":"LINE: Large-scale Information Network Embedding","created":"2015-03-12","cited_by_count":582},{"id":"2005.11650","title":"Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks","created":"2020-05-24","cited_by_count":566},{"id":"1705.03122","title":"Convolutional Sequence to Sequence Learning","created":"2017-05-08","cited_by_count":552},{"id":"1909.03211","title":"Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View","created":"2019-09-07","cited_by_count":545},{"id":"1905.04757","title":"NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding","created":"2019-05-12","cited_by_count":533},{"id":"2006.10637","title":"Temporal Graph Networks for Deep Learning on Dynamic Graphs","created":"2020-06-18","cited_by_count":530},{"id":"1611.05267","title":"Temporal Convolutional Networks for Action Segmentation and Detection","created":"2016-11-16","cited_by_count":508},{"id":"2111.14522","title":"Understanding over-squashing and bottlenecks on graphs via curvature","created":"2021-11-29","cited_by_count":478},{"id":"2002.07962","title":"Inductive Representation Learning on Temporal Graphs","created":"2020-02-19","cited_by_count":462},{"id":"1709.05584","title":"Representation Learning on Graphs: Methods and Applications","created":"2017-09-17","cited_by_count":457},{"id":"1908.01207","title":"Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks","created":"2019-08-03","cited_by_count":448},{"id":"1902.10191","title":"EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs","created":"2019-02-26","cited_by_count":447},{"id":"1905.11136","title":"Provably Powerful Graph Networks","created":"2019-05-27","cited_by_count":389},{"id":"1711.07553","title":"Residual Gated Graph ConvNets","created":"2017-11-20","cited_by_count":346},{"id":"1811.00855","title":"Session-based Recommendation with Graph Neural Networks","created":"2018-11-01","cited_by_count":312},{"id":"1909.12223","title":"PairNorm: Tackling Oversmoothing in GNNs","created":"2019-09-26","cited_by_count":297},{"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":"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":"1709.07604","title":"A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications","created":"2017-09-22","cited_by_count":260},{"id":"2103.00550","title":"A Survey on Deep Semi-supervised Learning","created":"2021-02-28","cited_by_count":251},{"id":"2006.13318","title":"A Note on Over-Smoothing for Graph Neural Networks","created":"2020-06-23","cited_by_count":249},{"id":"1511.05298","title":"Structural-RNN: Deep Learning on Spatio-Temporal Graphs","created":"2015-11-17","cited_by_count":234},{"id":"2106.07178","title":"A Comprehensive Survey on Graph Anomaly Detection with Deep Learning","created":"2021-06-14","cited_by_count":230},{"id":"1907.05321","title":"Time2Vec: Learning a Vector Representation of Time","created":"2019-07-11","cited_by_count":213},{"id":"2006.06830","title":"Data Augmentation for Graph Neural Networks","created":"2020-06-11","cited_by_count":203},{"id":"2101.05974","title":"Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks","created":"2021-01-15","cited_by_count":193},{"id":"2202.02541","title":"TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials","created":"2022-02-05","cited_by_count":182},{"id":"2001.05140","title":"Graph-Bert: Only Attention is Needed for Learning Graph Representations","created":"2020-01-15","cited_by_count":177},{"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":"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":"2005.03675","title":"Machine Learning on Graphs: A Model and Comprehensive Taxonomy","created":"2020-05-07","cited_by_count":156},{"id":"2001.06362","title":"Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks","created":"2020-01-17","cited_by_count":153},{"id":"2302.11636","title":"Do We Really Need Complicated Model Architectures For Temporal Networks?","created":"2023-02-22","cited_by_count":143},{"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":"2202.08455","title":"Transformer for Graphs: An Overview from Architecture Perspective","created":"2022-02-17","cited_by_count":107},{"id":"1805.11273","title":"DynGEM: Deep Embedding Method for Dynamic Graphs","created":"2018-05-29","cited_by_count":102},{"id":"2106.04319","title":"Breaking the Limits of Message Passing Graph Neural Networks","created":"2021-06-08","cited_by_count":101},{"id":"1908.09710","title":"Variational Graph Recurrent Neural Networks","created":"2019-08-26","cited_by_count":87},{"id":"2105.13495","title":"Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention","created":"2021-05-27","cited_by_count":87},{"id":"1704.06199","title":"Dynamic Graph Convolutional Networks","created":"2017-04-20","cited_by_count":83},{"id":"2302.04181","title":"Attending to Graph Transformers","created":"2023-02-08","cited_by_count":83},{"id":"2011.11545","title":"APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding","created":"2020-11-23","cited_by_count":78},{"id":"2105.07944","title":"TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning","created":"2021-05-17","cited_by_count":68},{"id":"2007.03113","title":"Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks","created":"2020-07-06","cited_by_count":64},{"id":"2110.13094","title":"Gophormer: Ego-Graph Transformer for Node Classification","created":"2021-10-25","cited_by_count":63},{"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":"2106.09876","title":"Anomaly Detection in Dynamic Graphs via Transformer","created":"2021-06-18","cited_by_count":59},{"id":"2002.01038","title":"Gated Graph Recurrent Neural Networks","created":"2020-02-03","cited_by_count":58},{"id":"2010.09891","title":"Robust Optimization as Data Augmentation for Large-scale Graphs","created":"2020-10-19","cited_by_count":58},{"id":"2104.07368","title":"Dynamic Graph Neural Networks for Sequential Recommendation","created":"2021-04-15","cited_by_count":57},{"id":"1905.03994","title":"Predicting Path Failure In Time-Evolving Graphs","created":"2019-05-10","cited_by_count":54},{"id":"1911.12864","title":"Self-attention with Functional Time Representation Learning","created":"2019-11-28","cited_by_count":52},{"id":"1805.11921","title":"Anonymous Walk Embeddings","created":"2018-05-30","cited_by_count":46},{"id":"2109.12218","title":"Long-Range Transformers for Dynamic Spatiotemporal Forecasting","created":"2021-09-24","cited_by_count":46},{"id":"1903.08889","title":"Node Embedding over Temporal Graphs","created":"2019-03-21","cited_by_count":45},{"id":"2010.09834","title":"Topology-Aware Graph Pooling Networks","created":"2020-10-19","cited_by_count":44},{"id":"2103.15447","title":"Dynamic Network Embedding Survey","created":"2021-03-29","cited_by_count":44},{"id":"2005.02844","title":"TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation","created":"2020-05-06","cited_by_count":33},{"id":"2101.02844","title":"Dynamic Graph Collaborative Filtering","created":"2021-01-08","cited_by_count":33},{"id":"2203.15209","title":"OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks","created":"2022-03-29","cited_by_count":33},{"id":"2201.02435","title":"Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning","created":"2022-01-07","cited_by_count":32},{"id":"2111.08900","title":"A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction","created":"2021-11-17","cited_by_count":28}],"next_cursor":"100"}