Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs

18 December 2022

Papers citing "Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs"

8 / 8 papers shown

Title
Wide & Deep Learning for Node Classification Yancheng Chen Wenguo Yang Zhipeng Jiang GNN 24 0 0 04 May 2025
Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition Xin Gao Tong Chen Wentao Zhang Junliang Yu Guanhua Ye Quoc Viet Hung Nguyen 24 7 0 22 May 2024
Mixture of Weak & Strong Experts on Graphs Hanqing Zeng Hanjia Lyu Diyi Hu Yinglong Xia Jiebo Luo 23 3 0 09 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 19 0 16 Aug 2023
GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks Wentao Zhao Qitian Wu Chenxiao Yang Junchi Yan 17 12 0 20 Jun 2023
MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization Xiaotian Han Tong Zhao Yozen Liu Xia Hu Neil Shah GNN 31 36 0 30 Sep 2022
Geom-GCN: Geometric Graph Convolutional Networks Hongbin Pei Bingzhen Wei Kevin Chen-Chuan Chang Yu Lei Bo Yang GNN 167 1,058 0 13 Feb 2020
Representation Learning on Graphs with Jumping Knowledge Networks Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi Stefanie Jegelka GNN 226 1,935 0 09 Jun 2018