Not too little, not too much: a theoretical analysis of graph (over)smoothing

24 May 2022

Papers citing "Not too little, not too much: a theoretical analysis of graph (over)smoothing"

5 / 55 papers shown

Title
Unsupervised Optimal Power Flow Using Graph Neural Networks Damian Owerko Fernando Gama Alejandro Ribeiro 23 15 0 17 Oct 2022
Gradient Gating for Deep Multi-Rate Learning on Graphs T. Konstantin Rusch B. Chamberlain Michael W. Mahoney Michael M. Bronstein Siddhartha Mishra 74 53 0 02 Oct 2022
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges M. Bronstein Joan Bruna Taco S. Cohen Petar Velivcković GNN 166 1,095 0 27 Apr 2021
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
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 231 3,202 0 24 Nov 2016