{"total":22,"rows":[{"id":"2310.18884","title":"Simple and Asymmetric Graph Contrastive Learning without Augmentations","created":"2023-10-29","cited_by_count":38,"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":"2307.04937","title":"Towards Fair Graph Neural Networks via Graph Counterfactual","created":"2023-07-10","cited_by_count":23,"area":null},{"id":"2406.07955","title":"How Interpretable Are Interpretable Graph Neural Networks?","created":"2024-06-12","cited_by_count":13,"area":null},{"id":"2508.07117","title":"From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context","created":"2025-08-09","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":"2407.13251","title":"Motif-Consistent Counterfactuals with Adversarial Refinement for Graph-Level Anomaly Detection","created":"2024-07-18","cited_by_count":3,"area":null},{"id":"2507.00440","title":"A Recipe for Causal Graph Regression: Confounding Effects Revisited","created":"2025-07-01","cited_by_count":3,"area":null},{"id":"2308.00391","title":"Counterfactual Graph Transformer for Traffic Flow Prediction","created":"2023-08-01","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":"2404.06090","title":"Fair Graph Neural Network with Supervised Contrastive Regularization","created":"2024-04-09","cited_by_count":1,"area":null},{"id":"2410.14522","title":"Rethinking Distance Metrics for Counterfactual Explainability","created":"2024-10-18","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":"2508.14683","title":"Improving Fairness in Graph Neural Networks via Counterfactual Debiasing","created":"2025-08-20","cited_by_count":1,"area":null},{"id":"2508.10785","title":"Enhancing Fairness in Autoencoders for Node-Level Graph Anomaly Detection","created":"2025-08-14","cited_by_count":0,"area":null},{"id":"2601.22593","title":"Heterogeneous Graph Alignment for Joint Reasoning and Interpretability","created":"2026-01-30","cited_by_count":0,"area":null},{"id":"2605.00374","title":"Advancing Edge Classification through High-Dimensional Causal Modeling of Node-Edge Interplay","created":"2026-05-01","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":"2608.16038","title":"NICE: Scale-Stable Perturbations for Graph Neural Network Explanations via Noise Corruption","created":"2026-08-17","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":"2609.11170","title":"From Explanations to Interventions: Execution-Guided Counterfactual Synthesis in Temporal Graphs","created":"2026-09-10","cited_by_count":0,"area":null},{"id":"2609.37667","title":"Where Privacy Belongs: Placement Diagnosis and Certified Selection for Private Counterfactual Explanations on Graphs","created":"2026-09-29","cited_by_count":0,"area":null}],"next_cursor":null}