Approximation Ratios of Graph Neural Networks for Combinatorial Problems

24 May 2019

Papers citing "Approximation Ratios of Graph Neural Networks for Combinatorial Problems"

30 / 30 papers shown

Title
NN-Steiner: A Mixed Neural-algorithmic Approach for the Rectilinear Steiner Minimum Tree Problem Andrew B. Kahng Robert Nerem Yusu Wang Chien-Yi Yang 22 5 0 17 Dec 2023
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning Hanxuan Yang Qingchao Kong Wenji Mao BDL 28 0 0 09 Dec 2023
Going beyond persistent homology using persistent homology Johanna Immonen Amauri H. Souza Vikas K. Garg 41 9 0 10 Nov 2023
The Expressive Power of Graph Neural Networks: A Survey Bingxue Zhang Changjun Fan Shixuan Liu Kuihua Huang Xiang Zhao Jin-Yu Huang Zhong Liu 40 20 0 16 Aug 2023
Solving the Kidney-Exchange Problem via Graph Neural Networks with No Supervision P. F. Pimenta Pedro H. C. Avelar Luís C. Lamb 25 1 0 19 Apr 2023
Equivariant Polynomials for Graph Neural Networks Omri Puny Derek Lim B. Kiani Haggai Maron Y. Lipman 30 31 0 22 Feb 2023
Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion Haotian Ju Dongyue Li Aneesh Sharma Hongyang R. Zhang 31 40 0 09 Feb 2023
Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure Ryoma Sato 37 5 0 26 Jan 2023
Learning Graph Algorithms With Recurrent Graph Neural Networks Florian Grötschla Joël Mathys Roger Wattenhofer GNN 19 6 0 09 Dec 2022
GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation Han Huang Leilei Sun Bowen Du Yanjie Fu Weifeng Lv DiffM 32 42 0 04 Dec 2022
Provably expressive temporal graph networks Amauri Souza Diego Mesquita Samuel Kaski Vikas K. Garg 89 55 0 29 Sep 2022
On Representing Linear Programs by Graph Neural Networks Ziang Chen Jialin Liu Xinshang Wang Jian Lu W. Yin AI4CE 60 31 0 25 Sep 2022
Theory of Graph Neural Networks: Representation and Learning Stefanie Jegelka GNN AI4CE 33 68 0 16 Apr 2022
A Theoretical Comparison of Graph Neural Network Extensions Pál András Papp Roger Wattenhofer 103 45 0 30 Jan 2022
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks Pál András Papp Karolis Martinkus Lukas Faber Roger Wattenhofer GNN 28 139 0 11 Nov 2021
Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters Takanori Maehara Hoang NT 41 2 0 05 Nov 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 24 65 0 12 Jun 2021
Breaking the Limits of Message Passing Graph Neural Networks M. Balcilar Pierre Héroux Benoit Gaüzère Pascal Vasseur Sébastien Adam P. Honeine 21 121 0 08 Jun 2021
Combinatorial optimization and reasoning with graph neural networks Quentin Cappart Didier Chételat Elias Boutros Khalil Andrea Lodi Christopher Morris Petar Velickovic AI4CE 32 348 0 18 Feb 2021
Graph Neural Networks: Taxonomy, Advances and Trends Yu Zhou Haixia Zheng Xin Huang Shufeng Hao Dengao Li Jumin Zhao AI4TS 25 116 0 16 Dec 2020
GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training Tianle Cai Shengjie Luo Keyulu Xu Di He Tie-Yan Liu Liwei Wang GNN 32 159 0 07 Sep 2020
Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art Yun Peng Byron Choi Jianliang Xu 25 5 0 26 Aug 2020
Expressive Power of Invariant and Equivariant Graph Neural Networks Waïss Azizian Marc Lelarge 28 112 0 28 Jun 2020
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting Giorgos Bouritsas Fabrizio Frasca S. Zafeiriou M. Bronstein 58 424 0 16 Jun 2020
How hard is to distinguish graphs with graph neural networks? Andreas Loukas GNN 25 6 0 13 May 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 57 321 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 33 5,406 0 20 Dec 2018