Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs

Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs

9 June 2023

Lirong Wu

Stan Z. Li

Papers citing "Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs"

7 / 7 papers shown

Title
Sparse Decomposition of Graph Neural Networks Yaochen Hu Mai Zeng Ge Zhang P. Rumiantsev Liheng Ma Yingxue Zhang Mark Coates 27 0 0 25 Oct 2024
Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrasting Lirong Wu Haitao Lin Guojiang Zhao Cheng Tan Stan Z. Li 28 0 0 09 Sep 2024
A Teacher-Free Graph Knowledge Distillation Framework with Dual Self-Distillation Lirong Wu Haitao Lin Zhangyang Gao Guojiang Zhao Stan Z. Li 38 8 0 06 Mar 2024
MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding Lirong Wu Yijun Tian Yufei Huang Siyuan Li Haitao Lin Nitesh V. Chawla Stan Z. Li 26 22 0 22 Feb 2024
Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification Lirong Wu Jun-Xiong Xia Haitao Lin Zhangyang Gao Zicheng Liu Guojiang Zhao Stan Z. Li 61 6 0 05 Oct 2022
Iterative Graph Self-Distillation Hanlin Zhang Shuai Lin Weiyang Liu Pan Zhou Jian Tang Xiaodan Liang Eric P. Xing SSL 52 33 0 23 Oct 2020
Distilling Knowledge from Graph Convolutional Networks Yiding Yang Jiayan Qiu Mingli Song Dacheng Tao Xinchao Wang 146 226 0 23 Mar 2020