UNR-Explainer: Counterfactual Explanations for Unsupervised Node Representation Learning Models
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Keywords: XAI, Unsupervised node representation learning, Counterfactual Explanations
Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2024/AuthorGuide.
TL;DR: UNR-Explainer aims to provide counterfactual explanations for a single target node in unsupervised node representation models.
Abstract: Node representation learning, such as Graph Neural Networks (GNNs), has become one of the important learning methods in machine learning, and the demand for reliable explanation generation is growing. Despite extensive research on explanation generation for supervised node representation learning, explaining unsupervised models has been less explored. To address this gap, we propose a method for generating counterfactual (CF) explanations in unsupervised node representation learning, aiming to identify the most important subgraphs that cause a significant change in the $k$-nearest neighbors of a node of interest in the learned embedding space upon perturbation. The $k$-nearest neighbor-based CF explanation method provides simple, yet pivotal, information for understanding unsupervised downstream tasks, such as top-$k$ link prediction and clustering. Furthermore, we introduce a Monte Carlo Tree Search (MCTS)-based explainability method for generating expressive CF explanations for **U**nsupervised **N**ode **R**epresentation learning methods, which we call **UNR-Explainer**. The proposed method demonstrates improved performance on six datasets for both unsupervised GraphSAGE and DGI.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors' identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Primary Area: visualization or interpretation of learned representations
Submission Number: 8687
Loading