Higher-Order Explanations of Graph Neural Networks via Relevant Walks

5 June 2020

Papers citing "Higher-Order Explanations of Graph Neural Networks via Relevant Walks"

50 / 111 papers shown

Title
A Survey on Explainability of Graph Neural Networks Jaykumar Kakkad Jaspal Jannu Kartik Sharma Charu C. Aggarwal Sourav Medya 28 23 0 02 Jun 2023
DEGREE: Decomposition Based Explanation For Graph Neural Networks Qizhang Feng Ninghao Liu Fan Yang Ruixiang Tang Mengnan Du Xia Hu 23 22 0 22 May 2023
Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness Hogun Park Jennifer Neville 14 4 0 24 Apr 2023
Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability Indro Spinelli Michele Guerra F. Bianchi Simone Scardapane 30 0 0 14 Apr 2023
Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks Lorenz Linhardt Klaus-Robert Muller G. Montavon AAML 23 7 0 12 Apr 2023
Rethinking the Trigger-injecting Position in Graph Backdoor Attack Jing Xu Gorka Abad S. Picek LLMSV SILM 19 6 0 05 Apr 2023
DAG Matters! GFlowNets Enhanced Explainer For Graph Neural Networks Wenqian Li Yinchuan Li Zhigang Li Jianye Hao Yan Pang 88 29 0 04 Mar 2023
Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment Tianxiang Zhao Dongsheng Luo Xiang Zhang Suhang Wang 26 7 0 07 Jan 2023
Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching Fang Wu Siyuan Li Xurui Jin Yinghui Jiang Dragomir R. Radev Z. Niu Stan Z. Li 24 10 0 07 Jan 2023
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces Pattarawat Chormai J. Herrmann Klaus-Robert Muller G. Montavon FAtt 45 17 0 30 Dec 2022
FAIR AI Models in High Energy Physics Javier Mauricio Duarte Haoyang Li Avik Roy Ruike Zhu Eliu A. Huerta ... Mark S. Neubauer Sang Eon Park M. Quinnan R. Rusack Zhizhen Zhao 30 8 0 09 Dec 2022
Towards Explainable Motion Prediction using Heterogeneous Graph Representations Sandra Carrasco Limeros Sylwia Majchrowska Joakim Johnander Christoffer Petersson David Fernández Llorca 29 15 0 07 Dec 2022
Do graph neural networks learn traditional jet substructure? Farouk Mokhtar Raghav Kansal Javier Mauricio Duarte GNN 34 11 0 17 Nov 2022
PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks Yong-Min Shin Sun-Woo Kim Won-Yong Shin 31 4 0 31 Oct 2022
Explaining the Explainers in Graph Neural Networks: a Comparative Study Antonio Longa Steve Azzolin G. Santin G. Cencetti Pietro Lio' Bruno Lepri Andrea Passerini 46 27 0 27 Oct 2022
Toward Multiple Specialty Learners for Explaining GNNs via Online Knowledge Distillation Tien-Cuong Bui Van-Duc Le Wen-Syan Li S. Cha 15 3 0 20 Oct 2022
A Detailed Study of Interpretability of Deep Neural Network based Top Taggers Ayush Khot Mark S. Neubauer Avik Roy AAML 33 16 0 09 Oct 2022
L2XGNN: Learning to Explain Graph Neural Networks G. Serra Mathias Niepert 33 7 0 28 Sep 2022
Defending Against Backdoor Attack on Graph Nerual Network by Explainability B. Jiang Zhao Li AAML GNN 50 16 0 07 Sep 2022
Higher-order Clustering and Pooling for Graph Neural Networks Alexandre Duval Fragkiskos D. Malliaros 29 38 0 02 Sep 2022
An Explainer for Temporal Graph Neural Networks Wenchong He Minh Nhat Vu Zhe Jiang My T. Thai AI4TS 9 17 0 02 Sep 2022
FlowX: Towards Explainable Graph Neural Networks via Message Flows Shurui Gui Hao Yuan Jie Wang Qicheng Lao Kang Li Shuiwang Ji 32 11 0 26 Jun 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 24 49 0 20 Jun 2022
From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagation Reduan Achtibat Maximilian Dreyer Ilona Eisenbraun S. Bosse Thomas Wiegand Wojciech Samek Sebastian Lapuschkin FAtt 27 131 0 07 Jun 2022
EiX-GNN : Concept-level eigencentrality explainer for graph neural networks Adrien Raison Pascal Bourdon David Helbert 9 1 0 07 Jun 2022
Deep Ensembles for Graphs with Higher-order Dependencies Steven J. Krieg William C. Burgis Patrick M. Soga Nitesh V. Chawla AI4CE GNN 19 3 0 27 May 2022
Towards Faithful and Consistent Explanations for Graph Neural Networks Tianxiang Zhao Dongsheng Luo Xiang Zhang Suhang Wang FAtt 48 18 0 27 May 2022
ES-GNN: Generalizing Graph Neural Networks Beyond Homophily with Edge Splitting Jingwei Guo Kaizhu Huang Rui Zhang Xinping Yi AAML 28 4 0 27 May 2022
Towards Explanation for Unsupervised Graph-Level Representation Learning Qinghua Zheng Jihong Wang Minnan Luo Yaoliang Yu Jundong Li L. Yao Xiao Chang 16 1 0 20 May 2022
Trustworthy Graph Neural Networks: Aspects, Methods and Trends He Zhang Bang Wu Xingliang Yuan Shirui Pan Hanghang Tong Jian Pei 45 104 0 16 May 2022
Reinforced Causal Explainer for Graph Neural Networks Xiang Wang Y. Wu An Zhang Fuli Feng Xiangnan He Tat-Seng Chua CML 17 46 0 23 Apr 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 29 131 0 18 Apr 2022
Automatic Identification of Chemical Moieties Jonas Lederer M. Gastegger Kristof T. Schütt Michael C. Kampffmeyer Klaus-Robert Muller Oliver T. Unke 21 5 0 30 Mar 2022
GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs Alan Perotti P. Bajardi Francesco Bonchi Andre' Panisson FAtt 11 8 0 17 Feb 2022
XAI for Transformers: Better Explanations through Conservative Propagation Ameen Ali Thomas Schnake Oliver Eberle G. Montavon Klaus-Robert Muller Lior Wolf FAtt 15 88 0 15 Feb 2022
MotifExplainer: a Motif-based Graph Neural Network Explainer Zhaoning Yu Hongyang Gao 33 15 0 01 Feb 2022
Visualizing the Diversity of Representations Learned by Bayesian Neural Networks Dennis Grinwald Kirill Bykov Shinichi Nakajima Marina M.-C. Höhne 23 5 0 26 Jan 2022
Deconfounding to Explanation Evaluation in Graph Neural Networks Yingmin Wu Xiang Wang An Zhang Xia Hu Fuli Feng Xiangnan He Tat-Seng Chua FAtt CML 17 14 0 21 Jan 2022
Toward Explainable AI for Regression Models S. Letzgus Patrick Wagner Jonas Lederer Wojciech Samek Klaus-Robert Muller G. Montavon XAI 28 63 0 21 Dec 2021
Forecasting Brain Activity Based on Models of Spatio-Temporal Brain Dynamics: A Comparison of Graph Neural Network Architectures S. Wein Alina Schüller A. Tomé W. Malloni M. Greenlee E. Lang AI4CE 30 14 0 08 Dec 2021
Explaining machine-learned particle-flow reconstruction Farouk Mokhtar Raghav Kansal Daniel Madrigal Diaz Javier Mauricio Duarte J. Pata M. Pierini J. Vlimant AI4CE 23 17 0 24 Nov 2021
Explaining GNN over Evolving Graphs using Information Flow Yazheng Liu Xi Zhang Sihong Xie FAtt 9 0 0 19 Nov 2021
A Practical guide on Explainable AI Techniques applied on Biomedical use case applications Adrien Bennetot Ivan Donadello Ayoub El Qadi M. Dragoni Thomas Frossard ... M. Trocan Raja Chatila Andreas Holzinger Artur Garcez Natalia Díaz Rodríguez XAI 24 7 0 13 Nov 2021
Graph Denoising with Framelet Regularizer Bingxin Zhou Ruikun Li Xuebin Zheng Yu Guang Wang Junbin Gao 13 14 0 05 Nov 2021
An Empirical Study: Extensive Deep Temporal Point Process Haitao Lin Cheng Tan Lirong Wu Zhangyang Gao Stan. Z. Li AI4TS 13 12 0 19 Oct 2021
Graph-guided random forest for gene set selection Bastian Pfeifer Hubert Baniecki Anna Saranti P. Biecek Andreas Holzinger 39 18 0 26 Aug 2021
GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks Lucie Charlotte Magister Dmitry Kazhdan Vikash Singh Pietro Lió 27 48 0 25 Jul 2021
Algorithmic Concept-based Explainable Reasoning Dobrik Georgiev Pietro Barbiero Dmitry Kazhdan Petar Velivcković Pietro Lió 69 16 0 15 Jul 2021
Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods Chirag Agarwal Marinka Zitnik Himabindu Lakkaraju 19 51 0 16 Jun 2021
SEEN: Sharpening Explanations for Graph Neural Networks using Explanations from Neighborhoods Hyeoncheol Cho Youngrock Oh Eunjoo Jeon FAtt 19 0 0 16 Jun 2021