Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks

20 May 2025

Papers citing "Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks"

23 / 23 papers shown

Title
How Interpretable Are Interpretable Graph Neural Networks? Yongqiang Chen Yatao Bian Bo Han James Cheng 61 6 0 12 Jun 2024
Generating Robust Counterfactual Witnesses for Graph Neural Networks Dazhuo Qiu Mengying Wang Arijit Khan Yinghui Wu 63 3 0 30 Apr 2024
Adaptive Hypergraph Network for Trust Prediction Rongwei Xu Guanfeng Liu Yan Wang Xuyun Zhang Kai Zheng Xiaofang Zhou 37 1 0 07 Feb 2024
Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks Zhuomin Chen Jiaxing Zhang Jingchao Ni Xiaoting Li Yuchen Bian Md. Mezbahul Islam A. Mondal Hua Wei Dongsheng Luo 59 2 0 03 Feb 2024
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs Pengfei Ding Yan Wang Guanfeng Liu Nan Wang Xiaofang Zhou OODD OOD 46 4 0 07 Jan 2024
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion Jialin Chen Shirley Wu Abhijit Gupta Rex Ying DiffM 50 5 0 30 Oct 2023
Evaluating Explainability for Graph Neural Networks Chirag Agarwal Owen Queen Himabindu Lakkaraju Marinka Zitnik 68 104 0 19 Aug 2022
GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks Kenza Amara Rex Ying Zitao Zhang Zhihao Han Yinan Shan U. Brandes S. Schemm Ce Zhang 45 51 0 20 Jun 2022
GOOD: A Graph Out-of-Distribution Benchmark Shurui Gui Xiner Li Limei Wang Shuiwang Ji OOD 62 117 0 16 Jun 2022
Discovering Invariant Rationales for Graph Neural Networks Yingmin Wu Xiang Wang An Zhang Xiangnan He Tat-Seng Chua OOD AI4CE 140 226 0 30 Jan 2022
Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization Kartik Ahuja Ethan Caballero Dinghuai Zhang Jean-Christophe Gagnon-Audet Yoshua Bengio Ioannis Mitliagkas Irina Rish OOD 42 258 0 11 Jun 2021
DIG: A Turnkey Library for Diving into Graph Deep Learning Research Meng Liu Youzhi Luo Limei Wang Yaochen Xie Haonan Yuan ... Haoran Liu Cong Fu Bora Oztekin Xuan Zhang Shuiwang Ji GNN 60 120 0 23 Mar 2021
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks Ana Lucic Maartje ter Hoeve Gabriele Tolomei Maarten de Rijke Fabrizio Silvestri 132 145 0 05 Feb 2021
Parameterized Explainer for Graph Neural Network Dongsheng Luo Wei Cheng Dongkuan Xu Wenchao Yu Bo Zong Haifeng Chen Xiang Zhang 120 546 0 09 Nov 2020
PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks Minh Nhat Vu My T. Thai BDL 41 330 0 12 Oct 2020
XGNN: Towards Model-Level Explanations of Graph Neural Networks Haonan Yuan Jiliang Tang Xia Hu Shuiwang Ji 59 392 0 03 Jun 2020
Decision-Making with Auto-Encoding Variational Bayes Romain Lopez Pierre Boyeau Nir Yosef Michael I. Jordan Jeffrey Regier BDL 182 10,591 0 17 Feb 2020
Wasserstein Weisfeiler-Lehman Graph Kernels Matteo Togninalli M. Ghisu Felipe Llinares-López Bastian Rieck Karsten Borgwardt 53 196 0 04 Jun 2019
GNNExplainer: Generating Explanations for Graph Neural Networks Rex Ying Dylan Bourgeois Jiaxuan You Marinka Zitnik J. Leskovec LLMAG 109 1,300 0 10 Mar 2019
Fast Graph Representation Learning with PyTorch Geometric Matthias Fey J. E. Lenssen 3DH GNN 3DPC 155 4,289 0 06 Mar 2019
Mean-field theory of graph neural networks in graph partitioning T. Kawamoto Masashi Tsubaki T. Obuchi 48 58 0 29 Oct 2018
GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders M. Simonovsky N. Komodakis GNN BDL 81 842 0 09 Feb 2018
Graph Attention Networks Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio Yoshua Bengio GNN 301 19,902 0 30 Oct 2017