Distill n' Explain: explaining graph neural networks using simple surrogates

17 March 2023

Papers citing "Distill n' Explain: explaining graph neural networks using simple surrogates"

5 / 5 papers shown

Title
Interpretability of Graph Neural Networks to Assess Effects of Global Change Drivers on Ecological Networks Emré Anakok Pierre Barbillon Colin Fontaine Elisa Thébault 45 0 0 19 Mar 2025
Trustworthy Graph Neural Networks: Aspects, Methods and Trends He Zhang Bang Wu Xingliang Yuan Shirui Pan Hanghang Tong Jian Pei 36 98 0 16 May 2022
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks Ana Lucic Maartje ter Hoeve Gabriele Tolomei Maarten de Rijke Fabrizio Silvestri 107 87 0 05 Feb 2021
Explainability in Graph Neural Networks: A Taxonomic Survey Hao Yuan Haiyang Yu Shurui Gui Shuiwang Ji 159 463 0 31 Dec 2020
Distilling Knowledge from Graph Convolutional Networks Yiding Yang Jiayan Qiu Mingli Song Dacheng Tao Xinchao Wang 141 222 0 23 Mar 2020