Breaking the Expressive Bottlenecks of Graph Neural Networks

14 December 2020

Papers citing "Breaking the Expressive Bottlenecks of Graph Neural Networks"

8 / 8 papers shown

Title
Graph Neural Networks-based Hybrid Framework For Predicting Particle Crushing Strength Tongya Zheng Tianli Zhang Qingzheng Guan Wenjie Huang Zunlei Feng Min-Gyoo Song Chun-Yen Chen AI4CE 31 1 0 26 Jul 2023
Node-wise Hardware Trojan Detection Based on Graph Learning Kento Hasegawa Kazuki Yamashita Seira Hidano Kazuhide Fukushima Kazuo Hashimoto N. Togawa 19 24 0 04 Dec 2021
First Place Solution of KDD Cup 2021 & OGB Large-Scale Challenge Graph Prediction Track Chengxuan Ying Mingqi Yang Shuxin Zheng Guolin Ke Shengjie Luo Tianle Cai Chenglin Wu Yuxin Wang Yanming Shen Di He 16 11 0 15 Jun 2021
Do Transformers Really Perform Bad for Graph Representation? Chengxuan Ying Tianle Cai Shengjie Luo Shuxin Zheng Guolin Ke Di He Yanming Shen Tie-Yan Liu GNN 28 433 0 09 Jun 2021
A Survey on The Expressive Power of Graph Neural Networks Ryoma Sato 184 172 0 09 Mar 2020
Representation Learning on Graphs with Jumping Knowledge Networks Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi Stefanie Jegelka GNN 267 1,945 0 09 Jun 2018
MoleculeNet: A Benchmark for Molecular Machine Learning Zhenqin Wu Bharath Ramsundar Evan N. Feinberg Joseph Gomes C. Geniesse Aneesh S. Pappu K. Leswing Vijay S. Pande OOD 175 1,778 0 02 Mar 2017
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 253 3,239 0 24 Nov 2016