UNR-Explainer: Counterfactual Explanations for Unsupervised Node Representation Learning Models

TL;DR

This paper introduces UNR-Explainer, a novel method for generating counterfactual explanations for unsupervised node embedding models like GraphSAGE and DGI, enhancing interpretability in graph learning tasks.

Contribution

It presents a new approach using Monte Carlo Tree Search to produce expressive counterfactual explanations for unsupervised node representations, addressing a gap in explainability.

Findings

Superior performance on diverse datasets

Effective identification of influential subgraphs

Enhanced understanding of unsupervised graph models

Abstract

Node representation learning, such as Graph Neural Networks (GNNs), has emerged as a pivotal method in machine learning. The demand for reliable explanation generation surges, yet unsupervised models remain underexplored. To bridge this gap, we introduce a method for generating counterfactual (CF) explanations in unsupervised node representation learning. We 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. Consequently, we introduce UNR-Explainer for generating expressive CF explanations for Unsupervised Node Representation learning methods based on a Monte Carlo Tree Search (MCTS). The proposed method demonstrates superior performance on diverse datasets for unsupervised GraphSAGE and DGI.