What graph neural networks cannot learn: depth vs width

6 July 2019

Papers citing "What graph neural networks cannot learn: depth vs width"

28 / 78 papers shown

Title
Nested Graph Neural Networks Muhan Zhang Pan Li 29 165 0 25 Oct 2021
From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness Lingxiao Zhao Wei Jin Leman Akoglu Neil Shah GNN 29 162 0 07 Oct 2021
Equivariant Subgraph Aggregation Networks Beatrice Bevilacqua Fabrizio Frasca Derek Lim Balasubramaniam Srinivasan Chen Cai G. Balamurugan M. Bronstein Haggai Maron 65 177 0 06 Oct 2021
Graph Neural Networks: Methods, Applications, and Opportunities Lilapati Waikhom Ripon Patgiri GNN 42 42 0 24 Aug 2021
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks Zhiqian Chen Fanglan Chen Lei Zhang Taoran Ji Kaiqun Fu Liang Zhao Feng Chen Lingfei Wu Charu C. Aggarwal Chang-Tien Lu 53 18 0 21 Jul 2021
Graph Neural Networks with Local Graph Parameters Pablo Barceló Floris Geerts Juan L. Reutter Maksimilian Ryschkov 29 65 0 12 Jun 2021
Scalars are universal: Equivariant machine learning, structured like classical physics Soledad Villar D. Hogg Kate Storey-Fisher Weichi Yao Ben Blum-Smith PINN AI4CE 29 131 0 11 Jun 2021
Skeleton-based Hand-Gesture Recognition with Lightweight Graph Convolutional Networks H. Sahbi 3DH GNN 21 3 0 09 Apr 2021
Combinatorial optimization and reasoning with graph neural networks Quentin Cappart Didier Chételat Elias Boutros Khalil Andrea Lodi Christopher Morris Petar Velickovic AI4CE 37 352 0 18 Feb 2021
Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization Aseem Baranwal K. Fountoulakis Aukosh Jagannath OODD 41 75 0 13 Feb 2021
Interpreting and Unifying Graph Neural Networks with An Optimization Framework Meiqi Zhu Xiao Wang C. Shi Houye Ji Peng Cui AI4CE 54 198 0 28 Jan 2021
Boost then Convolve: Gradient Boosting Meets Graph Neural Networks Sergei Ivanov Liudmila Prokhorenkova AI4CE 53 52 0 21 Jan 2021
Graph Neural Networks: Taxonomy, Advances and Trends Yu Zhou Haixia Zheng Xin Huang Shufeng Hao Dengao Li Jumin Zhao AI4TS 27 117 0 16 Dec 2020
Learning to Drop: Robust Graph Neural Network via Topological Denoising Dongsheng Luo Wei Cheng Wenchao Yu Bo Zong Jingchao Ni Haifeng Chen Xiang Zhang OOD 21 259 0 13 Nov 2020
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning Muhan Zhang Pan Li Yinglong Xia Kai Wang Long Jin 24 186 0 30 Oct 2020
Expressive Power of Invariant and Equivariant Graph Neural Networks Waïss Azizian Marc Lelarge 28 110 0 28 Jun 2020
Hierarchical Inter-Message Passing for Learning on Molecular Graphs Matthias Fey Jan-Gin Yuen F. Weichert GNN 41 86 0 22 Jun 2020
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting Giorgos Bouritsas Fabrizio Frasca S. Zafeiriou M. Bronstein 63 426 0 16 Jun 2020
How hard is to distinguish graphs with graph neural networks? Andreas Loukas GNN 27 6 0 13 May 2020
Machine Learning on Graphs: A Model and Comprehensive Taxonomy Ines Chami Sami Abu-El-Haija Bryan Perozzi Christopher Ré Kevin Patrick Murphy 24 287 0 07 May 2020
Let's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework Floris Geerts Filip Mazowiecki Guillermo A. Pérez GNN 29 38 0 06 Apr 2020
Universal Function Approximation on Graphs Rickard Brüel-Gabrielsson 32 6 0 14 Mar 2020
Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks Zhiqian Chen Fanglan Chen Lei Zhang Taoran Ji Kaiqun Fu Liang Zhao Feng Chen Lingfei Wu Charu Aggarwal Chang-Tien Lu 29 45 0 27 Feb 2020
Generalization and Representational Limits of Graph Neural Networks Vikas K. Garg Stefanie Jegelka Tommi Jaakkola GNN 26 304 0 14 Feb 2020
Can Graph Neural Networks Count Substructures? Zhengdao Chen Lei Chen Soledad Villar Joan Bruna GNN 61 321 0 10 Feb 2020
Random Features Strengthen Graph Neural Networks Ryoma Sato M. Yamada H. Kashima GNN AAML 13 232 0 08 Feb 2020
Graph Neural Networks: A Review of Methods and Applications Jie Zhou Ganqu Cui Shengding Hu Zhengyan Zhang Cheng Yang Zhiyuan Liu Lifeng Wang Changcheng Li Maosong Sun AI4CE GNN 43 5,416 0 20 Dec 2018
Interaction Networks for Learning about Objects, Relations and Physics Peter W. Battaglia Razvan Pascanu Matthew Lai Danilo Jimenez Rezende Koray Kavukcuoglu AI4CE OCL PINN GNN 283 1,401 0 01 Dec 2016