Evaluating Explainability for Graph Neural Networks

19 August 2022

Himabindu Lakkaraju

Papers citing "Evaluating Explainability for Graph Neural Networks"

48 / 48 papers shown

Title
PointExplainer: Towards Transparent Parkinson's Disease Diagnosis Xuechao Wang S. Nõmm Junqing Huang Kadri Medijainen A. Toomela Michael Ruzhansky AAML FAtt 14 0 0 04 May 2025
Interpretable graph-based models on multimodal biomedical data integration: A technical review and benchmarking Alireza Sadeghi F. Hajati A. Argha Nigel H Lovell Min Yang Hamid Alinejad-Rokny 27 0 0 03 May 2025
STX-Search: Explanation Search for Continuous Dynamic Spatio-Temporal Models Saif Anwar Nathan Griffiths T. Popham A. Bhalerao AI4TS LRM 62 0 0 06 Mar 2025
GNN-XAR: A Graph Neural Network for Explainable Activity Recognition in Smart Homes Michele Fiori Davide Mor Gabriele Civitarese Claudio Bettini 36 0 0 25 Feb 2025
Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations Laurin Lux Alexander H. Berger Maria Romeo Tricas Alaa E. Fayed S. Linus Kreitner Jonas Weidner M. Menten Daniel Rueckert Johannes C. Paetzold 33 2 0 23 Feb 2025
No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets Corinna Coupette Jeremy Wayland Emily Simons Bastian Alexander Rieck 66 1 0 04 Feb 2025
On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach Ruichu Cai Yuxuan Zhu Xuexin Chen Yuan Fang Min-man Wu Jie Qiao Z. Hao 43 7 0 31 Dec 2024
The GECo algorithm for Graph Neural Networks Explanation Salvatore Calderaro Domenico Amato Giosuè Lo Bosco R. Rizzo Filippo Vella 67 0 0 18 Nov 2024
Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces Siqi Chen Zhiqiang Wang Xianqi Deng Yili Shen C. Ju ... Lin Xiong Guo Ling Dieaa Alhmoud Hui Guan Zhou Lin 26 0 0 03 Nov 2024
Explaining Hypergraph Neural Networks: From Local Explanations to Global Concepts Shiye Su Iulia Duta Lucie Charlotte Magister Pietro Lio' FAtt 24 0 0 10 Oct 2024
The FIX Benchmark: Extracting Features Interpretable to eXperts Helen Jin Shreya Havaldar Chaehyeon Kim Anton Xue Weiqiu You ... Bhuvnesh Jain Amin Madani M. Sako Lyle Ungar Eric Wong 24 0 0 20 Sep 2024
Revisiting FunnyBirds evaluation framework for prototypical parts networks Szymon Opłatek Dawid Rymarczyk Bartosz Zieliñski 20 3 0 21 Aug 2024
Graph Neural Network Causal Explanation via Neural Causal Models Arman Behnam Binghui Wang CML 34 3 0 12 Jul 2024
Towards Understanding Sensitive and Decisive Patterns in Explainable AI: A Case Study of Model Interpretation in Geometric Deep Learning Jiajun Zhu Siqi Miao Rex Ying Pan Li 27 1 0 30 Jun 2024
Generating Human Understandable Explanations for Node Embeddings Zohair Shafi Ayan Chatterjee Tina Eliassi-Rad 21 1 0 11 Jun 2024
Explainable Graph Neural Networks Under Fire Zhong Li Simon Geisler Yuhang Wang Stephan Günnemann M. Leeuwen AAML 34 0 0 10 Jun 2024
Explaining Graph Neural Networks via Structure-aware Interaction Index Ngoc H. Bui Hieu Trung Nguyen Viet Anh Nguyen Rex Ying FAtt 32 1 0 23 May 2024
Iterative Graph Neural Network Enhancement via Frequent Subgraph Mining of Explanations Harish Naik Jan Polster Raj Shekhar Tamás L. Horváth Gyorgy Turán 26 3 0 12 Mar 2024
PAC Learnability under Explanation-Preserving Graph Perturbations Xu Zheng Farhad Shirani Tianchun Wang Shouwei Gao Wenqian Dong Wei Cheng Dongsheng Luo 14 0 0 07 Feb 2024
Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks Jiahua Rao Jiancong Xie Hanjing Lin Shuangjia Zheng Zhen Wang Yuedong Yang 8 0 0 07 Feb 2024
PowerGraph: A power grid benchmark dataset for graph neural networks Anna Varbella Kenza Amara B. Gjorgiev Mennatallah El-Assady G. Sansavini 8 4 0 05 Feb 2024
Explainability-Based Adversarial Attack on Graphs Through Edge Perturbation Dibaloke Chanda Saba Heidari Gheshlaghi Nasim Yahya Soltani AAML 13 0 0 28 Dec 2023
A Unified Pre-training and Adaptation Framework for Combinatorial Optimization on Graphs Ruibin Zeng Minglong Lei Lingfeng Niu Lan Cheng AI4CE 11 0 0 16 Dec 2023
Generative Explanations for Graph Neural Network: Methods and Evaluations Jialin Chen Kenza Amara Junchi Yu Rex Ying 23 3 0 09 Nov 2023
Graph AI in Medicine Ruth Johnson Michelle M. Li Ayush Noori Owen Queen Marinka Zitnik 16 3 0 20 Oct 2023
AttributionLab: Faithfulness of Feature Attribution Under Controllable Environments Yang Zhang Yawei Li Hannah Brown Mina Rezaei Bernd Bischl Philip H. S. Torr Ashkan Khakzar Kenji Kawaguchi OOD 33 1 0 10 Oct 2023
Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks Xu Zheng Farhad Shirani Tianchun Wang Wei Cheng Zhuomin Chen Haifeng Chen Hua Wei Dongsheng Luo 24 7 0 03 Oct 2023
GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking Mert Kosan S. Verma Burouj Armgaan Khushbu Pahwa Ambuj K. Singh Sourav Medya Sayan Ranu 16 13 0 03 Oct 2023
GInX-Eval: Towards In-Distribution Evaluation of Graph Neural Network Explanations Kenza Amara Mennatallah El-Assady Rex Ying 20 4 0 28 Sep 2023
How Faithful are Self-Explainable GNNs? Marc Christiansen Lea Villadsen Zhiqiang Zhong Stefano Teso Davide Mottin 16 3 0 29 Aug 2023
Evaluating Link Prediction Explanations for Graph Neural Networks Claudio Borile Alan Perotti Andre' Panisson FAtt 30 2 0 03 Aug 2023
Is Task-Agnostic Explainable AI a Myth? Alicja Chaszczewicz 21 2 0 13 Jul 2023
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions Fang Li Yi Nian Zenan Sun Cui Tao LM&MA OOD AI4TS AI4CE 22 5 0 18 Jun 2023
On the Interplay of Subset Selection and Informed Graph Neural Networks Niklas Breustedt Paolo Climaco Jochen Garcke J. Hamaekers Gitta Kutyniok D. Lorenz Rick Oerder Chirag Varun Shukla 17 0 0 15 Jun 2023
Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency Owen Queen Thomas Hartvigsen Teddy Koker Huan He Theodoros Tsiligkaridis Marinka Zitnik AI4TS 34 16 0 03 Jun 2023
Robust Ante-hoc Graph Explainer using Bilevel Optimization Kha-Dinh Luong Mert Kosan A. Silva Ambuj K. Singh 24 6 0 25 May 2023
Similarity of Neural Network Models: A Survey of Functional and Representational Measures Max Klabunde Tobias Schumacher M. Strohmaier Florian Lemmerich 43 63 0 10 May 2023
Unstructured and structured data: Can we have the best of both worlds with large language models? W. Tan 6 1 0 25 Apr 2023
Counterfactual Learning on Graphs: A Survey Zhimeng Guo Teng Xiao Zongyu Wu Charu C. Aggarwal Hui Liu Suhang Wang CML AI4CE 38 18 0 03 Apr 2023
Towards Training GNNs using Explanation Directed Message Passing V. Giunchiglia Chirag Varun Shukla Guadalupe Gonzalez Chirag Agarwal 20 5 0 30 Nov 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 39 27 0 27 Oct 2022
Global Explainability of GNNs via Logic Combination of Learned Concepts Steve Azzolin Antonio Longa Pietro Barbiero Pietro Lio' Andrea Passerini 17 54 0 13 Oct 2022
A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics Yiqiao Li Jianlong Zhou Sunny Verma Fang Chen XAI 18 34 0 26 Jul 2022
Graph Neural Networks for Graphs with Heterophily: A Survey Xin-Yang Zheng Yi Wang Yixin Liu Ming Li Miao Zhang Di Jin Philip S. Yu Shirui Pan 11 213 0 14 Feb 2022
Benchmarking the Combinatorial Generalizability of Complex Query Answering on Knowledge Graphs Zihao W. Wang Hang Yin Yangqiu Song 24 29 0 18 Sep 2021
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
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks Minjie Wang Da Zheng Zihao Ye Quan Gan Mufei Li ... J. Zhao Haotong Zhang Alex Smola Jinyang Li Zheng-Wei Zhang AI4CE GNN 182 731 0 03 Sep 2019