From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness

7 October 2021

Papers citing "From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness"

25 / 25 papers shown

Title
Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Xiang Wang Muhan Zhang 182 0 0 04 Feb 2025
Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements Haoyang Li Yongjun Xu C. Zhang Alexander Zhou Lei Chen Qing Li AI4CE 317 0 0 03 Jan 2025
Revisiting Random Walks for Learning on Graphs Jinwoo Kim Olga Zaghen Ayhan Suleymanzade Youngmin Ryou Seunghoon Hong 109 1 0 01 Jul 2024
The Expressive Power of Graph Neural Networks: A Survey Bingxue Zhang Changjun Fan Shixuan Liu Kuihua Huang Xiang Zhao Jin-Yu Huang Zhong Liu 172 23 0 16 Aug 2023
Nested Graph Neural Networks Muhan Zhang Pan Li 76 167 0 25 Oct 2021
Ego-GNNs: Exploiting Ego Structures in Graph Neural Networks Dylan Sandfelder Priyesh Vijayan William L. Hamilton 46 26 0 22 Jul 2021
TUDataset: A collection of benchmark datasets for learning with graphs Christopher Morris Nils M. Kriege Franka Bause Kristian Kersting Petra Mutzel Marion Neumann 216 819 0 16 Jul 2020
Hierarchical Inter-Message Passing for Learning on Molecular Graphs Matthias Fey Jan-Gin Yuen F. Weichert GNN 73 86 0 22 Jun 2020
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting Giorgos Bouritsas Fabrizio Frasca Stefanos Zafeiriou M. Bronstein 109 430 0 16 Jun 2020
Open Graph Benchmark: Datasets for Machine Learning on Graphs Weihua Hu Matthias Fey Marinka Zitnik Yuxiao Dong Hongyu Ren Bowen Liu Michele Catasta J. Leskovec 283 2,725 0 02 May 2020
Benchmarking Graph Neural Networks Vijay Prakash Dwivedi Chaitanya K. Joshi Anh Tuan Luu T. Laurent Yoshua Bengio Xavier Bresson 385 939 0 02 Mar 2020
Random Features Strengthen Graph Neural Networks Ryoma Sato M. Yamada H. Kashima GNN AAML 68 237 0 08 Feb 2020
Coloring graph neural networks for node disambiguation George Dasoulas Ludovic Dos Santos Kevin Scaman Aladin Virmaux 65 82 0 12 Dec 2019
On the equivalence between graph isomorphism testing and function approximation with GNNs Zhengdao Chen Soledad Villar Lei Chen Joan Bruna 78 281 0 29 May 2019
Universal Invariant and Equivariant Graph Neural Networks Nicolas Keriven Gabriel Peyré 158 292 0 13 May 2019
Relational Pooling for Graph Representations R. Murphy Balasubramaniam Srinivasan Vinayak A. Rao Bruno Ribeiro GNN 110 261 0 06 Mar 2019
Fast Graph Representation Learning with PyTorch Geometric Matthias Fey J. E. Lenssen 3DH GNN 3DPC 204 4,334 0 06 Mar 2019
A Comprehensive Survey on Graph Neural Networks Zonghan Wu Shirui Pan Fengwen Chen Guodong Long Chengqi Zhang Philip S. Yu FaML GNN AI4TS AI4CE 644 8,496 0 03 Jan 2019
A Modern Take on the Bias-Variance Tradeoff in Neural Networks Brady Neal Sarthak Mittal A. Baratin Vinayak Tantia Matthew Scicluna Simon Lacoste-Julien Ioannis Mitliagkas 80 169 0 19 Oct 2018
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks Christopher Morris Martin Ritzert Matthias Fey William L. Hamilton J. E. Lenssen Gaurav Rattan Martin Grohe GNN 177 1,630 0 04 Oct 2018
How Powerful are Graph Neural Networks? Keyulu Xu Weihua Hu J. Leskovec Stefanie Jegelka GNN 220 7,623 0 01 Oct 2018
Geometric deep learning on graphs and manifolds using mixture model CNNs Federico Monti Davide Boscaini Jonathan Masci Emanuele Rodolà Jan Svoboda M. Bronstein GNN 398 1,819 0 25 Nov 2016
node2vec: Scalable Feature Learning for Networks Aditya Grover J. Leskovec 183 10,856 0 03 Jul 2016
Learning Convolutional Neural Networks for Graphs Mathias Niepert Mohamed Ahmed Konstantin Kutzkov GNN SSL 120 2,151 0 17 May 2016
Convolutional Networks on Graphs for Learning Molecular Fingerprints David Duvenaud D. Maclaurin J. Aguilera-Iparraguirre Rafael Gómez-Bombarelli Timothy D. Hirzel Alán Aspuru-Guzik Ryan P. Adams GNN 199 3,345 0 30 Sep 2015