BAGEL: A Benchmark for Assessing Graph Neural Network Explanations

28 June 2022

Papers citing "BAGEL: A Benchmark for Assessing Graph Neural Network Explanations"

8 / 8 papers shown

Title
Benchmarking XAI Explanations with Human-Aligned Evaluations Rémi Kazmierczak Steve Azzolin Eloise Berthier Anna Hedström Patricia Delhomme ... Goran Frehse Massimiliano Mancini Baptiste Caramiaux Andrea Passerini Gianni Franchi 23 1 0 04 Nov 2024
PAGE: Parametric Generative Explainer for Graph Neural Network Yang Qiu Wei Liu Jun Wang Ruixuan Li BDL 28 0 0 26 Aug 2024
xAI-Drop: Don't Use What You Cannot Explain Vincenzo Marco De Luca Antonio Longa Andrea Passerini Pietro Lio' 47 0 0 29 Jul 2024
ChaosMining: A Benchmark to Evaluate Post-Hoc Local Attribution Methods in Low SNR Environments Ge Shi Ziwen Kan J. Smucny Ian Davidson 56 0 0 17 Jun 2024
Inpainting the Gaps: A Novel Framework for Evaluating Explanation Methods in Vision Transformers Lokesh Badisa Sumohana S. Channappayya 45 0 0 17 Jun 2024
MEGAN: Multi-Explanation Graph Attention Network Jonas Teufel Luca Torresi Patrick Reiser Pascal Friederich 26 8 0 23 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 46 27 0 27 Oct 2022
Privacy and Transparency in Graph Machine Learning: A Unified Perspective Megha Khosla 21 4 0 22 Jul 2022