arXiv Machine Learning By Zongrui Li, Yuhang Zhao, Ying Zhao, Yuanzhao Guo, Qiang Huang, Yuan Tian

HiSE: A Lightweight Hierarchical Semantic Explainer for Heterogeneous Graph Neural Networks

Read the original on arXiv Machine Learning →

arXiv:2606. 03495v1 Announce Type: new Abstract: Heterogeneous graph neural networks (HGNNs) have demonstrated remarkable performance in modeling complex relational data, however their interpretability in high-stakes applications remains a critical challenge.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Sep 7

A Comparative Study of Counterfactual Explainers for Graph Neural Networks Enabling Multiple Types of Graph Edit

The paper presents a comparative analysis of six state‑of‑the‑art counterfactual explainers for graph neural networks, focusing on methods that can both add and remove edges to alter model predictions. It evaluates these explainers across diverse real‑world and synthetic datasets, covering binary and multi‑class graph and node classification tasks, using a range of quantitative and qualitative metrics. The study highlights the trade‑offs between explanation size, coverage, and quality, aiming to pinpoint each method’s strengths and weaknesses to inform future research.

By Maria Myrto Villia, Filippos Gouidis, Theodore Patkos, Panos Trahanias