Every Node Counts: Improving the Training of Graph Neural Networks on Node Classification

29 November 2022

Papers citing "Every Node Counts: Improving the Training of Graph Neural Networks on Node Classification"

8 / 8 papers shown

Title
Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration Xiao Wang Hongrui Liu Chuan Shi Cheng Yang UQCV 109 113 0 29 Sep 2021
Elastic Graph Neural Networks Xiaorui Liu W. Jin Yao Ma Yaxin Li Hua Liu Yiqi Wang Ming Yan Jiliang Tang 92 107 0 05 Jul 2021
Beyond Low-frequency Information in Graph Convolutional Networks Deyu Bo Xiao Wang C. Shi Huawei Shen GNN 97 562 0 04 Jan 2021
Geom-GCN: Geometric Graph Convolutional Networks Hongbin Pei Bingzhen Wei Kevin Chen-Chuan Chang Yu Lei Bo Yang GNN 169 1,080 0 13 Feb 2020
Multi-scale Attributed Node Embedding Benedek Rozemberczki Carl Allen Rik Sarkar GNN 148 837 0 28 Sep 2019
Representation Learning on Graphs with Jumping Knowledge Networks Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi Stefanie Jegelka GNN 279 1,944 0 09 Jun 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 251 1,811 0 25 Nov 2016
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 259 3,240 0 24 Nov 2016