v1v2 (latest)

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks

International Joint Conference on Artificial Intelligence (IJCAI), 2021

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 130 10 0 12 Jun 2024
Generating Robust Counterfactual Witnesses for Graph Neural Networks Dazhuo Qiu Mengying Wang Arijit Khan Yinghui Wu 153 3 0 30 Apr 2024
Adaptive Hypergraph Network for Trust Prediction Rongwei Xu Guanfeng Liu Yan Wang Xuyun Zhang Kai Zheng Xiaofang Zhou 80 2 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 156 4 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 157 5 0 07 Jan 2024
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion Jialin Chen Shirley Wu Abhijit Gupta Rex Ying DiffM 97 6 0 30 Oct 2023
Evaluating Explainability for Graph Neural Networks Chirag Agarwal Owen Queen Himabindu Lakkaraju Marinka Zitnik 144 133 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 100 61 0 20 Jun 2022
GOOD: A Graph Out-of-Distribution Benchmark Shurui Gui Xiner Li Limei Wang Shuiwang Ji OOD 172 137 0 16 Jun 2022
Discovering Invariant Rationales for Graph Neural Networks Yingmin Wu Xiang Wang An Zhang Xiangnan He Tat-Seng Chua OOD AI4CE 219 252 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 115 293 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 153 125 0 23 Mar 2021
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks Ana Lucic Maartje ter Hoeve Gabriele Tolomei Maarten de Rijke Fabrizio Silvestri 345 155 0 05 Feb 2021
Parameterized Explainer for Graph Neural Network Dongsheng Luo Wei Cheng Dongkuan Xu Wenchao Yu Bo Zong Haifeng Chen Xiang Zhang 191 611 0 09 Nov 2020
PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks Minh Nhat Vu My T. Thai BDL 133 361 0 12 Oct 2020
XGNN: Towards Model-Level Explanations of Graph Neural Networks Haonan Yuan Jiliang Tang Helen Zhou Shuiwang Ji 172 432 0 03 Jun 2020
Decision-Making with Auto-Encoding Variational Bayes Romain Lopez Pierre Boyeau Nir Yosef Michael I. Jordan Jeffrey Regier BDL 1.0K 10,591 0 17 Feb 2020
Wasserstein Weisfeiler-Lehman Graph Kernels Matteo Togninalli M. Ghisu Felipe Llinares-López Bastian Rieck Karsten Borgwardt 159 208 0 04 Jun 2019
GNNExplainer: Generating Explanations for Graph Neural Networks Rex Ying Dylan Bourgeois Jiaxuan You Marinka Zitnik J. Leskovec LLMAG 426 1,443 0 10 Mar 2019
Fast Graph Representation Learning with PyTorch Geometric Matthias Fey J. E. Lenssen 3DH GNN 3DPC 609 4,616 0 06 Mar 2019
Mean-field theory of graph neural networks in graph partitioning T. Kawamoto Masashi Tsubaki T. Obuchi 152 59 0 29 Oct 2018
GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders M. Simonovsky N. Komodakis GNN BDL 292 891 0 09 Feb 2018
Graph Attention Networks Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio Yoshua Bengio GNN 1.0K 21,568 0 30 Oct 2017