{"list":"other","total":74,"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":"1502.03167","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","created":"2015-02-11","cited_by_count":7005},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1607.06450","title":"Layer Normalization","created":"2016-07-21","cited_by_count":5669},{"id":"1810.00826","title":"How Powerful are Graph Neural Networks?","created":"2018-10-01","cited_by_count":4393},{"id":"1708.07747","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","created":"2017-08-25","cited_by_count":4349},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"1901.00596","title":"A Comprehensive Survey on Graph Neural Networks","created":"2019-01-03","cited_by_count":2919},{"id":"1903.02428","title":"Fast Graph Representation Learning with PyTorch Geometric","created":"2019-03-06","cited_by_count":2434},{"id":"2005.00687","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","created":"2020-05-02","cited_by_count":2163},{"id":"1703.06103","title":"Modeling Relational Data with Graph Convolutional Networks","created":"2017-03-17","cited_by_count":1694},{"id":"2104.13478","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","created":"2021-04-27","cited_by_count":1167},{"id":"1806.01973","title":"Graph Convolutional Neural Networks for Web-Scale Recommender Systems","created":"2018-06-06","cited_by_count":1138},{"id":"1801.07606","title":"Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning","created":"2018-01-22","cited_by_count":1102},{"id":"1811.05868","title":"Pitfalls of Graph Neural Network Evaluation","created":"2018-11-14","cited_by_count":1007},{"id":"1810.02244","title":"Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks","created":"2018-10-04","cited_by_count":881},{"id":"2002.05287","title":"Geom-GCN: Geometric Graph Convolutional Networks","created":"2020-02-13","cited_by_count":826},{"id":"1509.09292","title":"Convolutional Networks on Graphs for Learning Molecular Fingerprints","created":"2015-09-30","cited_by_count":754},{"id":"2006.05205","title":"On the Bottleneck of Graph Neural Networks and its Practical Implications","created":"2020-06-09","cited_by_count":631},{"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":"2111.14522","title":"Understanding over-squashing and bottlenecks on graphs via curvature","created":"2021-11-29","cited_by_count":478},{"id":"1911.05485","title":"Diffusion Improves Graph Learning","created":"2019-10-28","cited_by_count":414},{"id":"1905.11136","title":"Provably Powerful Graph Networks","created":"2019-05-27","cited_by_count":389},{"id":"1707.03815","title":"Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking","created":"2017-07-12","cited_by_count":335},{"id":"2006.09252","title":"Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting","created":"2020-06-16","cited_by_count":313},{"id":"1905.09550","title":"Revisiting Graph Neural Networks: All We Have is Low-Pass Filters","created":"2019-05-23","cited_by_count":261},{"id":"2006.07739","title":"DeeperGCN: All You Need to Train Deeper GCNs","created":"2020-06-13","cited_by_count":250},{"id":"2002.04025","title":"Can Graph Neural Networks Count Substructures?","created":"2020-02-10","cited_by_count":236},{"id":"2002.06157","title":"Generalization and Representational Limits of Graph Neural Networks","created":"2020-02-14","cited_by_count":218},{"id":"2106.12575","title":"Weisfeiler and Lehman Go Cellular: CW Networks","created":"2021-06-23","cited_by_count":218},{"id":"1907.03199","title":"What graph neural networks cannot learn: depth vs width","created":"2019-07-06","cited_by_count":201},{"id":"1905.04943","title":"Universal Invariant and Equivariant Graph Neural Networks","created":"2019-05-13","cited_by_count":183},{"id":"1905.12560","title":"On the equivalence between graph isomorphism testing and function approximation with GNNs","created":"2019-05-29","cited_by_count":172},{"id":"1901.09342","title":"On the Universality of Invariant Networks","created":"2019-01-27","cited_by_count":152},{"id":"2011.07057","title":"Learning to Drop: Robust Graph Neural Network via Topological Denoising","created":"2020-11-13","cited_by_count":127},{"id":"2112.09992","title":"Weisfeiler and Leman go Machine Learning: The Story so far","created":"2021-12-18","cited_by_count":122},{"id":"2005.06398","title":"Implicit Regularization in Deep Learning May Not Be Explainable by Norms","created":"2020-05-13","cited_by_count":119},{"id":"2102.06790","title":"A Unified Lottery Ticket Hypothesis for Graph Neural Networks","created":"2021-02-12","cited_by_count":118},{"id":"2106.04319","title":"Breaking the Limits of Message Passing Graph Neural Networks","created":"2021-06-08","cited_by_count":101},{"id":"2006.15646","title":"Expressive Power of Invariant and Equivariant Graph Neural Networks","created":"2020-06-28","cited_by_count":100},{"id":"1509.05009","title":"On the Expressive Power of Deep Learning: A Tensor Analysis","created":"2015-09-16","cited_by_count":97},{"id":"2204.04661","title":"Expressiveness and Approximation Properties of Graph Neural Networks","created":"2022-04-10","cited_by_count":64},{"id":"1605.06743","title":"Inductive Bias of Deep Convolutional Networks through Pooling Geometry","created":"2016-05-22","cited_by_count":53},{"id":"2208.03471","title":"Oversquashing in GNNs through the lens of information contraction and graph expansion","created":"2022-08-06","cited_by_count":53},{"id":"1907.05008","title":"Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology","created":"2019-07-11","cited_by_count":52},{"id":"2106.06707","title":"Graph Neural Networks with Local Graph Parameters","created":"2021-06-12","cited_by_count":52},{"id":"1801.00315","title":"Learning Relevant Features of Data with Multi-scale Tensor Networks","created":"2017-12-31","cited_by_count":51},{"id":"1803.09780","title":"Quantum Entanglement in Deep Learning Architectures","created":"2018-03-26","cited_by_count":50},{"id":"2201.12884","title":"A Theoretical Comparison of Graph Neural Network Extensions","created":"2022-01-30","cited_by_count":48},{"id":"2102.09972","title":"Implicit Regularization in Tensor Factorization","created":"2021-02-19","cited_by_count":44},{"id":"2006.12467","title":"The Depth-to-Width Interplay in Self-Attention","created":"2020-06-22","cited_by_count":42},{"id":"2003.03828","title":"$\\Pi-$nets: Deep Polynomial Neural Networks","created":"2020-03-08","cited_by_count":39},{"id":"2007.12035","title":"The expressive power of kth-order invariant graph networks","created":"2020-07-23","cited_by_count":34},{"id":"2110.04541","title":"The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design","created":"2021-10-09","cited_by_count":33},{"id":"1603.00162","title":"Convolutional Rectifier Networks as Generalized Tensor Decompositions","created":"2016-03-01","cited_by_count":32},{"id":"2004.13747","title":"Quantum-inspired Machine Learning on high-energy physics data","created":"2020-04-28","cited_by_count":31},{"id":"1711.00811","title":"Expressive power of recurrent neural networks","created":"2017-11-02","cited_by_count":30},{"id":"2010.15116","title":"On Graph Neural Networks versus Graph-Augmented MLPs","created":"2020-10-28","cited_by_count":30},{"id":"2201.11729","title":"Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks","created":"2022-01-27","cited_by_count":27},{"id":"1704.01552","title":"Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design","created":"2017-04-05","cited_by_count":24},{"id":"2004.02593","title":"Let's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework","created":"2020-04-06","cited_by_count":24},{"id":"2205.11691","title":"High-Order Pooling for Graph Neural Networks with Tensor Decomposition","created":"2022-05-24","cited_by_count":24},{"id":"2105.03928","title":"Which transformer architecture fits my data? A vocabulary bottleneck in self-attention","created":"2021-05-09","cited_by_count":16},{"id":"1710.09431","title":"On the Long-Term Memory of Deep Recurrent Networks","created":"2017-10-25","cited_by_count":14},{"id":"1905.01426","title":"Matrix Product State Based Quantum Classifier","created":"2019-05-04","cited_by_count":14},{"id":"1705.02302","title":"Analysis and Design of Convolutional Networks via Hierarchical Tensor Decompositions","created":"2017-05-05","cited_by_count":13},{"id":"1506.03059","title":"Deep SimNets","created":"2015-06-09","cited_by_count":10},{"id":"1601.00286","title":"Structure-Preserving Sparsification Methods for Social Networks","created":"2016-01-03","cited_by_count":10},{"id":"1901.10801","title":"Generalized Tensor Models for Recurrent Neural Networks","created":"2019-01-30","cited_by_count":8},{"id":"2204.05351","title":"Graph Ordering Attention Networks","created":"2022-04-11","cited_by_count":8},{"id":"1410.0781","title":"SimNets: A Generalization of Convolutional Networks","created":"2014-10-03","cited_by_count":5},{"id":"1703.06846","title":"Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions","created":"2017-03-20","cited_by_count":5}],"next_cursor":null}