{"list":"other","total":57,"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":"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":"1902.07153","title":"Simplifying Graph Convolutional Networks","created":"2019-02-19","cited_by_count":1477},{"id":"1801.07606","title":"Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning","created":"2018-01-22","cited_by_count":1102},{"id":"1611.08097","title":"Geometric deep learning: going beyond Euclidean data","created":"2016-11-24","cited_by_count":1048},{"id":"1810.05997","title":"Predict then Propagate: Graph Neural Networks meet Personalized PageRank","created":"2018-10-14","cited_by_count":993},{"id":"1603.08861","title":"Revisiting Semi-Supervised Learning with Graph Embeddings","created":"2016-03-29","cited_by_count":976},{"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":"2105.14491","title":"How Attentive are Graph Attention Networks?","created":"2021-05-30","cited_by_count":835},{"id":"2002.05287","title":"Geom-GCN: Geometric Graph Convolutional Networks","created":"2020-02-13","cited_by_count":826},{"id":"1907.10903","title":"DropEdge: Towards Deep Graph Convolutional Networks on Node Classification","created":"2019-07-25","cited_by_count":680},{"id":"1909.13021","title":"Multi-scale Attributed Node Embedding","created":"2019-09-28","cited_by_count":548},{"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":"2101.00797","title":"Beyond Low-frequency Information in Graph Convolutional Networks","created":"2021-01-04","cited_by_count":362},{"id":"1909.12223","title":"PairNorm: Tackling Oversmoothing in GNNs","created":"2019-09-26","cited_by_count":297},{"id":"1905.09550","title":"Revisiting Graph Neural Networks: All We Have is Low-Pass Filters","created":"2019-05-23","cited_by_count":261},{"id":"2106.10934","title":"GRAND: Graph Neural Diffusion","created":"2021-06-21","cited_by_count":258},{"id":"2006.13318","title":"A Note on Over-Smoothing for Graph Neural Networks","created":"2020-06-23","cited_by_count":249},{"id":"2103.03212","title":"Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks","created":"2021-03-04","cited_by_count":221},{"id":"2005.11079","title":"Graph Random Neural Network for Semi-Supervised Learning on Graphs","created":"2020-05-22","cited_by_count":213},{"id":"2106.07476","title":"Training Graph Neural Networks with 1000 Layers","created":"2021-06-14","cited_by_count":169},{"id":"1911.07532","title":"Graph Neural Ordinary Differential Equations","created":"2019-11-18","cited_by_count":154},{"id":"2010.02863","title":"Directional Graph Networks","created":"2020-10-06","cited_by_count":133},{"id":"2101.11859","title":"Interpreting and Unifying Graph Neural Networks with An Optimization Framework","created":"2021-01-28","cited_by_count":131},{"id":"1905.04497","title":"Stability Properties of Graph Neural Networks","created":"2019-05-11","cited_by_count":127},{"id":"2010.08853","title":"From Local Structures to Size Generalization in Graph Neural Networks","created":"2020-10-17","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":"1912.00967","title":"Continuous Graph Neural Networks","created":"2019-12-02","cited_by_count":114},{"id":"2006.06972","title":"Towards Deeper Graph Neural Networks with Differentiable Group Normalization","created":"2020-06-12","cited_by_count":114},{"id":"2106.04319","title":"Breaking the Limits of Message Passing Graph Neural Networks","created":"2021-06-08","cited_by_count":101},{"id":"1905.01004","title":"Stability and Generalization of Graph Convolutional Neural Networks","created":"2019-05-03","cited_by_count":81},{"id":"2008.01767","title":"Graph Neural Networks: Architectures, Stability and Transferability","created":"2020-08-04","cited_by_count":80},{"id":"2210.02997","title":"Expander Graph Propagation","created":"2022-10-06","cited_by_count":75},{"id":"2103.06064","title":"Graph Neural Networks Inspired by Classical Iterative Algorithms","created":"2021-03-10","cited_by_count":60},{"id":"2202.00645","title":"Generalization Analysis of Message Passing Neural Networks on Large Random Graphs","created":"2022-02-01","cited_by_count":59},{"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":"1810.03068","title":"Geometric Scattering for Graph Data Analysis","created":"2018-10-07","cited_by_count":49},{"id":"2102.06986","title":"How Framelets Enhance Graph Neural Networks","created":"2021-02-13","cited_by_count":46},{"id":"2201.12674","title":"Rewiring with Positional Encodings for Graph Neural Networks","created":"2022-01-29","cited_by_count":36},{"id":"1901.10524","title":"On the Transferability of Spectral Graph Filters","created":"2019-01-29","cited_by_count":28},{"id":"2102.09587","title":"Interpretable Stability Bounds for Spectral Graph Filters","created":"2021-02-18","cited_by_count":28},{"id":"2010.13747","title":"On the Stability of Graph Convolutional Neural Networks under Edge Rewiring","created":"2020-10-26","cited_by_count":25},{"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":"1907.04786","title":"Fast Haar Transforms for Graph Neural Networks","created":"2019-07-10","cited_by_count":20},{"id":"2103.14187","title":"Beyond Low-Pass Filters: Adaptive Feature Propagation on Graphs","created":"2021-03-26","cited_by_count":18},{"id":"2012.06922","title":"Decimated Framelet System on Graphs and Fast G-Framelet Transforms","created":"2020-12-12","cited_by_count":17},{"id":"2101.00079","title":"Graph Networks with Spectral Message Passing","created":"2020-12-31","cited_by_count":17},{"id":"1606.04429","title":"Using Fuzzy Logic to Leverage HTML Markup for Web Page Representation","created":"2016-06-14","cited_by_count":15},{"id":"1705.00442","title":"Filtering Random Graph Processes Over Random Time-Varying Graphs","created":"2017-05-01","cited_by_count":8},{"id":"2111.03264","title":"Graph Denoising with Framelet Regularizer","created":"2021-11-05","cited_by_count":8},{"id":"2202.04936","title":"Robust Graph Representation Learning for Local Corruption Recovery","created":"2022-02-10","cited_by_count":6},{"id":"2206.10385","title":"Approximate Equivariance SO(3) Needlet Convolution","created":"2022-06-17","cited_by_count":6}],"next_cursor":null}