L2XGNN: Learning to Explain Graph Neural Networks

28 September 2022

Papers citing "L2XGNN: Learning to Explain Graph Neural Networks"

6 / 6 papers shown

Title
From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module Claudio Battiloro Indro Spinelli Lev Telyatnikov Michael M. Bronstein Simone Scardapane P. Lorenzo BDL 18 14 0 25 May 2023
Graph neural networks for materials science and chemistry Patrick Reiser Marlen Neubert André Eberhard Luca Torresi Chen Zhou ... Houssam Metni Clint van Hoesel Henrik Schopmans T. Sommer Pascal Friederich GNN AI4CE 18 221 0 05 Aug 2022
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability Enyan Dai Tianxiang Zhao Huaisheng Zhu Jun Xu Zhimeng Guo Hui Liu Jiliang Tang Suhang Wang 14 123 0 18 Apr 2022
MotifExplainer: a Motif-based Graph Neural Network Explainer Zhaoning Yu Hongyang Gao 26 14 0 01 Feb 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