TraP2 A tool for generates high-fidelity explanations from GNN models

Introduction

Publications

1. Perturb more, trap more: Understanding behaviors of graph neural networks

   Cite this

   Chaojie Ji, Ruxin Wang, Hongyan Wu, 7 July, 2022 - Neurocomputing

Credits

1. Rui Zhang rui.zhang3@siat.ac.cn
   Investigator

   Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China

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