{"total":30,"rows":[{"id":"2107.04086","title":"Robust Counterfactual Explanations on Graph Neural Networks","created":"2021-07-08","cited_by_count":72,"area":null},{"id":"2210.08443","title":"CLEAR: Generative Counterfactual Explanations on Graphs","created":"2022-10-16","cited_by_count":48,"area":null},{"id":"2210.12089","title":"A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges","created":"2022-10-21","cited_by_count":30,"area":null},{"id":"2306.01958","title":"A Survey on Explainability of Graph Neural Networks","created":"2023-06-02","cited_by_count":29,"area":null},{"id":"2304.01391","title":"Counterfactual Learning on Graphs: A Survey","created":"2023-04-03","cited_by_count":22,"area":null},{"id":"2205.10014","title":"A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection","created":"2022-05-20","cited_by_count":20,"area":null},{"id":"2107.04119","title":"Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction","created":"2021-07-01","cited_by_count":15,"area":null},{"id":"2404.15687","title":"Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation","created":"2024-04-24","cited_by_count":12,"area":null},{"id":"2303.03951","title":"Probing Graph Representations","created":"2023-03-07","cited_by_count":10,"area":null},{"id":"2312.11747","title":"Robust Stochastic Graph Generator for Counterfactual Explanations","created":"2023-12-18","cited_by_count":7,"area":null},{"id":"2502.10556","title":"Recent Advances in Malware Detection: Graph Learning and Explainability","created":"2025-02-14","cited_by_count":7,"area":null},{"id":"2206.02957","title":"GRETEL: A unified framework for Graph Counterfactual Explanation Evaluation","created":"2022-06-07","cited_by_count":6,"area":null},{"id":"2312.12477","title":"When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook","created":"2023-12-19","cited_by_count":4,"area":null},{"id":"2404.12356","title":"Improving the interpretability of GNN predictions through conformal-based graph sparsification","created":"2024-04-18","cited_by_count":4,"area":null},{"id":"2406.13724","title":"Heterogeneous Graph Neural Networks with Post-hoc Explanations for Multi-modal and Explainable Land Use Inference","created":"2024-06-19","cited_by_count":3,"area":null},{"id":"2410.12156","title":"FragNet: A Graph Neural Network for Molecular Property Prediction with Four Levels of Interpretability","created":"2024-10-16","cited_by_count":3,"area":null},{"id":"2402.06030","title":"Game-theoretic Counterfactual Explanation for Graph Neural Networks","created":"2024-02-08","cited_by_count":2,"area":null},{"id":"2505.17542","title":"Graph Inverse Style Transfer for Counterfactual Explainability","created":"2025-05-23","cited_by_count":2,"area":null},{"id":"2308.02353","title":"Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes","created":"2023-08-04","cited_by_count":1,"area":null},{"id":"2403.06936","title":"Counterfactual Reasoning with Knowledge Graph Embeddings","created":"2024-03-11","cited_by_count":1,"area":null},{"id":"2504.02064","title":"From Text to Graph: Leveraging Graph Neural Networks for Enhanced Explainability in NLP","created":"2025-04-02","cited_by_count":1,"area":null},{"id":"2504.02606","title":"Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification","created":"2025-04-03","cited_by_count":1,"area":null},{"id":"2505.11396","title":"Finding Counterfactual Evidences for Node Classification","created":"2025-05-16","cited_by_count":1,"area":null},{"id":"2603.04209","title":"Beyond Edge Deletion: A Comprehensive Approach to Counterfactual Explanation in Graph Neural Networks","created":"2026-03-04","cited_by_count":1,"area":null},{"id":"2410.19978","title":"Global Graph Counterfactual Explanation: A Subgraph Mapping Approach","created":"2024-10-25","cited_by_count":0,"area":null},{"id":"2508.18561","title":"Enhancing Chemical Explainability Through Counterfactual Masking","created":"2025-08-25","cited_by_count":0,"area":null},{"id":"2511.22866","title":"ARM-Explainer -- Explaining and improving graph neural network predictions for the maximum clique problem using node features and association rule mining","created":"2025-11-28","cited_by_count":0,"area":null},{"id":"2606.22033","title":"A Completion-Aware Framework for Impactful Counterfactual Explainability in Graph Neural Networks","created":"2026-06-20","cited_by_count":0,"area":null},{"id":"2609.05113","title":"A Comparative Study of Counterfactual Explainers for Graph Neural Networks Enabling Multiple Types of Graph Edit","created":"2026-09-04","cited_by_count":0,"area":null},{"id":"2609.37650","title":"RACE: Relation-Level Counterfactual Explanations for Heterogeneous Graph Neural Networks","created":"2026-09-29","cited_by_count":0,"area":null}],"next_cursor":null}