{"total":65,"rows":[{"id":"2302.01018","title":"Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities","created":"2023-02-02","cited_by_count":61,"area":null},{"id":"2304.14068","title":"Interpretable Neural-Symbolic Concept Reasoning","created":"2023-04-27","cited_by_count":57,"area":null},{"id":"2210.15304","title":"Explaining the Explainers in Graph Neural Networks: a Comparative Study","created":"2022-10-27","cited_by_count":35,"area":null},{"id":"2304.01575","title":"The expressive power of pooling in Graph Neural Networks","created":"2023-04-04","cited_by_count":35,"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":"2310.01820","title":"Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks","created":"2023-10-03","cited_by_count":17,"area":null},{"id":"2406.15156","title":"Reconsidering Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs","created":"2024-06-21","cited_by_count":16,"area":null},{"id":"2310.01794","title":"GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking","created":"2023-10-03","cited_by_count":11,"area":null},{"id":"2502.02719","title":"Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective","created":"2025-02-04","cited_by_count":11,"area":null},{"id":"2401.14578","title":"GOAt: Explaining Graph Neural Networks via Graph Output Attribution","created":"2024-01-26","cited_by_count":10,"area":null},{"id":"2405.00205","title":"A Logic for Reasoning About Aggregate-Combine Graph Neural Networks","created":"2024-04-30","cited_by_count":10,"area":null},{"id":"2312.05596","title":"Factorized Explainer for Graph Neural Networks","created":"2023-12-09","cited_by_count":8,"area":null},{"id":"2405.12519","title":"MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation","created":"2024-05-21","cited_by_count":7,"area":null},{"id":"2406.07126","title":"Logical Distillation of Graph Neural Networks","created":"2024-06-11","cited_by_count":7,"area":null},{"id":"2412.00521","title":"A Self-Explainable Heterogeneous GNN for Relational Deep Learning","created":"2024-11-30","cited_by_count":7,"area":null},{"id":"2301.05860","title":"State of the Art and Potentialities of Graph-level Learning","created":"2023-01-14","cited_by_count":6,"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":"2308.15096","title":"How Faithful are Self-Explainable GNNs?","created":"2023-08-29","cited_by_count":5,"area":null},{"id":"2406.04612","title":"Faithful and Accurate Self-Attention Attribution for Message Passing Neural Networks via the Computation Tree Viewpoint","created":"2024-06-07","cited_by_count":5,"area":null},{"id":"2601.20815","title":"GNN Explanations that do not Explain and How to find Them","created":"2026-01-28","cited_by_count":5,"area":null},{"id":"2307.11688","title":"Interpretable Graph Networks Formulate Universal Algebra Conjectures","created":"2023-05-17","cited_by_count":4,"area":null},{"id":"2311.05764","title":"Generative Explanations for Graph Neural Network: Methods and Evaluations","created":"2023-11-09","cited_by_count":4,"area":null},{"id":"2403.07078","title":"Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning","created":"2024-03-11","cited_by_count":4,"area":null},{"id":"2405.12654","title":"Utilizing Description Logics for Global Explanations of Heterogeneous Graph Neural Networks","created":"2024-05-21","cited_by_count":4,"area":null},{"id":"2408.07340","title":"Towards Few-shot Self-explaining Graph Neural Networks","created":"2024-08-14","cited_by_count":4,"area":null},{"id":"2505.22252","title":"B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data","created":"2025-05-28","cited_by_count":4,"area":null},{"id":"2306.10447","title":"Globally Interpretable Graph Learning via Distribution Matching","created":"2023-06-18","cited_by_count":3,"area":null},{"id":"2407.20067","title":"xAI-Drop: Don't Use What You Cannot Explain","created":"2024-07-29","cited_by_count":3,"area":null},{"id":"2304.13010","title":"Unstructured and structured data: Can we have the best of both worlds with large language models?","created":"2023-04-25","cited_by_count":2,"area":null},{"id":"2309.01706","title":"On the Robustness of Post-hoc GNN Explainers to Label Noise","created":"2023-09-04","cited_by_count":2,"area":null},{"id":"2311.15112","title":"Everybody Needs a Little HELP: Explaining Graphs via Hierarchical Concepts","created":"2023-11-25","cited_by_count":2,"area":null},{"id":"2404.16532","title":"Global Concept Explanations for Graphs by Contrastive Learning","created":"2024-04-25","cited_by_count":2,"area":null},{"id":"2410.13469","title":"Interpreting Temporal Graph Neural Networks with Koopman Theory","created":"2024-10-17","cited_by_count":2,"area":null},{"id":"2509.18376","title":"GnnXemplar: Exemplars to Explanations -- Natural Language Rules for Global GNN Interpretability","created":"2025-09-22","cited_by_count":2,"area":null},{"id":"2511.11593","title":"Sound Logical Explanations for Mean Aggregation Graph Neural Networks","created":"2025-10-27","cited_by_count":2,"area":null},{"id":"2601.13404","title":"Local-to-Global Logical Explanations for Deep Vision Models","created":"2026-01-19","cited_by_count":2,"area":null},{"id":"2602.13583","title":"Differentiable Rule Induction from Raw Sequence Inputs","created":"2026-02-14","cited_by_count":2,"area":null},{"id":"2405.03009","title":"Explainable Malware Detection with Tailored Logic Explained Networks","created":"2024-05-05","cited_by_count":1,"area":null},{"id":"2406.08709","title":"Introducing Diminutive Causal Structure into Graph Representation Learning","created":"2024-06-13","cited_by_count":1,"area":null},{"id":"2410.12817","title":"Interactive Explainable Anomaly Detection for Industrial Settings","created":"2024-10-01","cited_by_count":1,"area":null},{"id":"2503.19476","title":"LogicXGNN: Grounded Logical Rules for Explaining Graph Neural Networks","created":"2025-03-25","cited_by_count":1,"area":null},{"id":"2508.11513","title":"Towards Faithful Class-level Self-explainability in Graph Neural Networks by Subgraph Dependencies","created":"2025-08-15","cited_by_count":1,"area":null},{"id":"2602.16947","title":"Beyond Message Passing: A Symbolic Alternative for Expressive and Interpretable Graph Learning","created":"2026-02-18","cited_by_count":1,"area":null},{"id":"2603.02025","title":"Revealing Combinatorial Reasoning of GNNs via Graph Concept Bottleneck Layer","created":"2026-03-02","cited_by_count":1,"area":"lr"},{"id":"2306.04835","title":"Empowering Counterfactual Reasoning over Graph Neural Networks through Inductivity","created":"2023-06-07","cited_by_count":0,"area":null},{"id":"2307.05150","title":"A Modal Logic for Explaining some Graph Neural Networks","created":"2023-07-11","cited_by_count":0,"area":null},{"id":"2405.06917","title":"Design Requirements for Human-Centered Graph Neural Network Explanations","created":"2024-05-11","cited_by_count":0,"area":null},{"id":"2408.14042","title":"PAGE: Parametric Generative Explainer for Graph Neural Network","created":"2024-08-26","cited_by_count":0,"area":null},{"id":"2411.02168","title":"Do graph neural network states contain graph properties?","created":"2024-11-04","cited_by_count":0,"area":null},{"id":"2503.09051","title":"Model-Level GNN Explanations via Rule-to-Graph Readout for Logit Reconstruction","created":"2025-03-12","cited_by_count":0,"area":null},{"id":"2505.24642","title":"WILTing Trees: Interpreting the Distance Between MPNN Embeddings","created":"2025-05-30","cited_by_count":0,"area":null},{"id":"2509.21699","title":"Exact Subgraph Isomorphism Network with Mixed $L_{0,2}$ Norm Constraint for Predictive Graph Mining","created":"2025-09-25","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":"2512.04530","title":"Explainable Graph Representation Learning via Graph Pattern Analysis","created":"2025-12-04","cited_by_count":0,"area":null},{"id":"2601.00877","title":"LearnAD: Learning Interpretable Rules for Brain Networks in Alzheimer's Disease Classification","created":"2025-12-30","cited_by_count":0,"area":null},{"id":"2604.11986","title":"Exploring Concept Subspace for Self-explainable Text-Attributed Graph Learning","created":"2026-04-13","cited_by_count":0,"area":null},{"id":"2604.18868","title":"Subgraph Concept Networks: Concept Levels in Graph Classification","created":"2026-04-20","cited_by_count":0,"area":null},{"id":"2604.20082","title":"Concept Graph Convolutions: Message Passing in the Concept Space","created":"2026-04-22","cited_by_count":0,"area":"lr"},{"id":"2605.14884","title":"AIMing for Standardised Explainability Evaluation in GNNs: A Framework and Case Study on Graph Kernel Networks","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2606.19735","title":"GLARE: A Natural Language Interface for Querying Global Explanations","created":"2026-06-18","cited_by_count":0,"area":null},{"id":"2607.04600","title":"Measuring What Matters: A Unified Evaluation Framework for GNN Explainability","created":"2026-07-06","cited_by_count":0,"area":null},{"id":"2608.14121","title":"Overcoming Shortcut Learning in Graph Neural Networks through Active Explanation Guidance","created":"2026-08-14","cited_by_count":0,"area":null},{"id":"2608.17103","title":"From Abductive Explanations to Global Logical Rules for Node Classification in SGCs","created":"2026-08-17","cited_by_count":0,"area":null},{"id":"2609.32436","title":"Controllable GNN Explanations via Multi-Metric Preference Selection","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2610.03526","title":"Beyond Trained Models: Compiling GNNs for a Sound Explainer Benchmark","created":"2026-10-02","cited_by_count":0,"area":null}],"next_cursor":null}