mlsanityData to 14 Oct 2026

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

arXiv 2605.17285 ↗First posted 17 May 2026cs.LG cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 25 of its 35 references that are in the map. First posted on arXiv 17 May 2026.

Builds on

25 of 35 references.

References · 25

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Inductive Representation Learning on Large Graphscited by 6,320
  3. 2018How Powerful are Graph Neural Networks?cited by 4,393
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  5. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  6. 2018Deep Graph Infomaxcited by 1,106
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13 more
  1. 2021CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networkscited by 119
  2. 2014Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)cited by 109
  3. 2021InfoGCL: Information-Aware Graph Contrastive Learningcited by 105
  4. 2022Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoningcited by 79
  5. 2021Robust Counterfactual Explanations on Graph Neural Networkscited by 72
  6. 2022CLEAR: Generative Counterfactual Explanations on Graphscited by 48
  7. 2021LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networkscited by 42
  8. 2022Global Counterfactual Explainer for Graph Neural Networkscited by 30
  9. 2023PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Predictioncited by 25
  10. 2022Task-Agnostic Graph Explanationscited by 20
  11. 2016Off the Beaten Path: Let's Replace Term-Based Retrieval with k-NN Searchcited by 14
  12. 2022Label-Free Explainability for Unsupervised Modelscited by 14
  13. 2023Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgenesscited by 4

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Citing papers per year: 2026: 1.

1 paper.

  1. 2026Self-supervised Adversarial Purification for Graph Neural Networkscited by 0