Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks

24 October 2022

Papers citing "Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks"

9 / 9 papers shown

Title
Teaching MLP More Graph Information: A Three-stage Multitask Knowledge Distillation Framework Junxian Li Bin Shi Erfei Cui Hua Wei Qinghua Zheng 43 0 0 02 Mar 2024
GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks Wentao Zhao Qitian Wu Chenxiao Yang Junchi Yan 24 12 0 20 Jun 2023
Knowledge Distillation on Graphs: A Survey Yijun Tian Shichao Pei Xiangliang Zhang Chuxu Zhang Nitesh V. Chawla 18 28 0 01 Feb 2023
Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs Chenxiao Yang Qitian Wu Jiahua Wang Junchi Yan AI4CE 19 51 0 18 Dec 2022
Cross-Task Knowledge Distillation in Multi-Task Recommendation Chenxiao Yang Junwei Pan Xiaofeng Gao Tingyu Jiang Dapeng Liu Guihai Chen 34 44 0 20 Feb 2022
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges M. Bronstein Joan Bruna Taco S. Cohen Petar Velivcković GNN 174 1,104 0 27 Apr 2021
Distilling Knowledge from Graph Convolutional Networks Yiding Yang Jiayan Qiu Mingli Song Dacheng Tao Xinchao Wang 160 226 0 23 Mar 2020
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 253 3,236 0 24 Nov 2016