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"

9 / 9 papers shown

Title
Sparse Decomposition of Graph Neural Networks Yaochen Hu Mai Zeng Ge Zhang Pavel Rumiantsev Liheng Ma Yingxue Zhang Mark Coates 37 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 37 0 0 09 Sep 2024
GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks Sukhdeep Singh Anuj Sharma Vinod Kumar Chauhan 39 0 0 22 Mar 2024
A Teacher-Free Graph Knowledge Distillation Framework with Dual Self-Distillation Lirong Wu Haitao Lin Zhangyang Gao Guojiang Zhao Stan Z. Li 45 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 Chawla Stan Z. Li 39 22 0 22 Feb 2024
LightHGNN: Distilling Hypergraph Neural Networks into MLPs for $100\times$ Faster Inference Yifan Feng Yihe Luo Shihui Ying Yue Gao BDL 37 2 0 06 Feb 2024
Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification Lirong Wu Jun 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 Xing SSL 62 33 0 23 Oct 2020
Distilling Knowledge from Graph Convolutional Networks Yiding Yang Jiayan Qiu Xiuming Zhang Dacheng Tao Xinchao Wang 166 226 0 23 Mar 2020