{"total":133,"rows":[{"id":"2206.03208","title":"From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagation","created":"2022-06-07","cited_by_count":141,"area":null},{"id":"2204.08570","title":"A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability","created":"2022-04-18","cited_by_count":118,"area":null},{"id":"2202.07304","title":"XAI for Transformers: Better Explanations through Conservative Propagation","created":"2022-02-15","cited_by_count":100,"area":null},{"id":"2205.07424","title":"Trustworthy Graph Neural Networks: Aspects, Methods and Trends","created":"2022-05-16","cited_by_count":89,"area":null},{"id":"2103.12608","title":"DIG: A Turnkey Library for Diving into Graph Deep Learning Research","created":"2021-03-23","cited_by_count":76,"area":null},{"id":"2206.09677","title":"GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks","created":"2022-06-20","cited_by_count":58,"area":null},{"id":"2310.16520","title":"Towards Self-Interpretable Graph-Level Anomaly Detection","created":"2023-10-25","cited_by_count":41,"area":null},{"id":"2106.09078","title":"Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods","created":"2021-06-16","cited_by_count":36,"area":null},{"id":"2105.08621","title":"Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks","created":"2021-05-18","cited_by_count":35,"area":null},{"id":"2107.11889","title":"GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks","created":"2021-07-25","cited_by_count":35,"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":"2204.11028","title":"Reinforced Causal Explainer for Graph Neural Networks","created":"2022-04-23","cited_by_count":34,"area":null},{"id":"2112.11407","title":"Toward Explainable AI for Regression Models","created":"2021-12-21","cited_by_count":31,"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":"2303.02448","title":"DAG Matters! GFlowNets Enhanced Explainer For Graph Neural Networks","created":"2023-03-04","cited_by_count":27,"area":null},{"id":"2310.19324","title":"TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery","created":"2023-10-30","cited_by_count":27,"area":null},{"id":"2209.03473","title":"Higher-order Clustering and Pooling for Graph Neural Networks","created":"2022-09-02","cited_by_count":26,"area":null},{"id":"2212.14855","title":"Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces","created":"2022-12-30","cited_by_count":25,"area":null},{"id":"2408.08041","title":"The Clever Hans Effect in Unsupervised Learning","created":"2024-08-15","cited_by_count":24,"area":null},{"id":"2206.12987","title":"FlowX: Towards Explainable Graph Neural Networks via Message Flows","created":"2022-06-26","cited_by_count":21,"area":null},{"id":"2305.12895","title":"DEGREE: Decomposition Based Explanation For Graph Neural Networks","created":"2023-05-22","cited_by_count":18,"area":null},{"id":"2102.13519","title":"PredDiff: Explanations and Interactions from Conditional Expectations","created":"2021-02-26","cited_by_count":17,"area":null},{"id":"2406.04280","title":"xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology","created":"2024-06-06","cited_by_count":16,"area":null},{"id":"2205.13700","title":"ES-GNN: Generalizing Graph Neural Networks Beyond Homophily with Edge Splitting","created":"2022-05-27","cited_by_count":13,"area":null},{"id":"2205.13733","title":"Towards Faithful and Consistent Explanations for Graph Neural Networks","created":"2022-05-27","cited_by_count":13,"area":null},{"id":"2501.16944","title":"Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks","created":"2025-01-28","cited_by_count":13,"area":null},{"id":"2107.07493","title":"Algorithmic Concept-based Explainable Reasoning","created":"2021-07-15","cited_by_count":12,"area":null},{"id":"2111.12840","title":"Explaining machine-learned particle-flow reconstruction","created":"2021-11-24","cited_by_count":12,"area":null},{"id":"2209.00807","title":"An Explainer for Temporal Graph Neural Networks","created":"2022-09-02","cited_by_count":12,"area":null},{"id":"2401.04829","title":"GNNShap: Scalable and Accurate GNN Explanation using Shapley Values","created":"2024-01-09","cited_by_count":12,"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":"2202.00519","title":"MotifExplainer: a Motif-based Graph Neural Network Explainer","created":"2022-02-01","cited_by_count":11,"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":"2405.14352","title":"Explaining Graph Neural Networks via Structure-aware Interaction Index","created":"2024-05-23","cited_by_count":11,"area":null},{"id":"2110.09823","title":"An Empirical Study: Extensive Deep Temporal Point Process","created":"2021-10-19","cited_by_count":10,"area":null},{"id":"2201.08802","title":"Deconfounding to Explanation Evaluation in Graph Neural Networks","created":"2022-01-21","cited_by_count":10,"area":null},{"id":"2212.03806","title":"Towards Explainable Motion Prediction using Heterogeneous Graph Representations","created":"2022-12-07","cited_by_count":10,"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":"2401.17441","title":"Explaining Predictive Uncertainty by Exposing Second-Order Effects","created":"2024-01-30","cited_by_count":10,"area":null},{"id":"2506.15538","title":"Capturing Polysemanticity with PRISM: A Multi-Concept Feature Description Framework","created":"2025-06-18","cited_by_count":10,"area":null},{"id":"2209.02902","title":"Defending Against Backdoor Attack on Graph Nerual Network by Explainability","created":"2022-09-07","cited_by_count":9,"area":null},{"id":"2304.05727","title":"Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks","created":"2023-04-12","cited_by_count":9,"area":null},{"id":"2405.06604","title":"Explaining Text Similarity in Transformer Models","created":"2024-05-10","cited_by_count":9,"area":null},{"id":"2111.03264","title":"Graph Denoising with Framelet Regularizer","created":"2021-11-05","cited_by_count":8,"area":null},{"id":"2301.02780","title":"Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching","created":"2023-01-07","cited_by_count":8,"area":null},{"id":"2311.14994","title":"Exploring Causal Learning through Graph Neural Networks: An In-depth Review","created":"2023-11-25","cited_by_count":8,"area":null},{"id":"2405.10995","title":"Higher-order Spatio-temporal Physics-incorporated Graph Neural Network for Multivariate Time Series Imputation","created":"2024-05-16","cited_by_count":8,"area":null},{"id":"2408.08754","title":"Self-Explainable Graph Transformer for Link Sign Prediction","created":"2024-08-16","cited_by_count":8,"area":null},{"id":"2209.14402","title":"L2XGNN: Learning to Explain Graph Neural Networks","created":"2022-09-28","cited_by_count":7,"area":null},{"id":"2210.04371","title":"A Detailed Study of Interpretability of Deep Neural Network based Top Taggers","created":"2022-10-09","cited_by_count":7,"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":"2408.17198","title":"Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features","created":"2024-08-30","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":"2506.14698","title":"Towards Desiderata-Driven Design of Visual Counterfactual Explainers","created":"2025-06-17","cited_by_count":7,"area":null},{"id":"2202.08815","title":"GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs","created":"2022-02-17","cited_by_count":6,"area":null},{"id":"2301.02791","title":"Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment","created":"2023-01-07","cited_by_count":6,"area":null},{"id":"2306.05760","title":"Efficient GNN Explanation via Learning Removal-based Attribution","created":"2023-06-09","cited_by_count":6,"area":null},{"id":"2406.02625","title":"Progressive Inference: Explaining Decoder-Only Sequence Classification Models Using Intermediate Predictions","created":"2024-06-03","cited_by_count":6,"area":null},{"id":"2410.08473","title":"Deeper Insights into Deep Graph Convolutional Networks: Stability and Generalization","created":"2024-10-11","cited_by_count":6,"area":null},{"id":"2210.17159","title":"PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks","created":"2022-10-31","cited_by_count":5,"area":null},{"id":"2308.12354","title":"Machine Learning Small Molecule Properties in Drug Discovery","created":"2023-08-02","cited_by_count":5,"area":null},{"id":"2312.15237","title":"Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network","created":"2023-12-23","cited_by_count":5,"area":null},{"id":"2405.01762","title":"EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time","created":"2024-05-02","cited_by_count":5,"area":null},{"id":"2406.06417","title":"Explainable Graph Neural Networks Under Fire","created":"2024-06-10","cited_by_count":5,"area":null},{"id":"2507.07544","title":"Position: We Need An Algorithmic Understanding of Generative AI","created":"2025-07-10","cited_by_count":5,"area":null},{"id":"2112.04266","title":"Forecasting Brain Activity Based on Models of Spatio-Temporal Brain Dynamics: A Comparison of Graph Neural Network Architectures","created":"2021-12-08","cited_by_count":4,"area":null},{"id":"2201.10859","title":"Visualizing the Diversity of Representations Learned by Bayesian Neural Networks","created":"2022-01-26","cited_by_count":4,"area":null},{"id":"2211.09912","title":"Do graph neural networks learn traditional jet substructure?","created":"2022-11-17","cited_by_count":4,"area":null},{"id":"2212.05081","title":"FAIR AI Models in High Energy Physics","created":"2022-12-09","cited_by_count":4,"area":null},{"id":"2304.02277","title":"Rethinking the Trigger-injecting Position in Graph Backdoor Attack","created":"2023-04-05","cited_by_count":4,"area":null},{"id":"2304.12036","title":"Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness","created":"2023-04-24","cited_by_count":4,"area":null},{"id":"2403.06425","title":"A Differential Geometric View and Explainability of GNN on Evolving Graphs","created":"2024-03-11","cited_by_count":4,"area":null},{"id":"2406.12841","title":"Demystifying Higher-Order Graph Neural Networks","created":"2024-06-18","cited_by_count":4,"area":null},{"id":"2511.16287","title":"Graph Diffusion Counterfactual Explanation","created":"2025-11-20","cited_by_count":4,"area":null},{"id":"2111.14260","title":"A Practical guide on Explainable AI Techniques applied on Biomedical use case applications","created":"2021-11-13","cited_by_count":3,"area":null},{"id":"2205.13988","title":"Deep Ensembles for Graphs with Higher-order Dependencies","created":"2022-05-27","cited_by_count":3,"area":null},{"id":"2304.07152","title":"Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability","created":"2023-04-14","cited_by_count":3,"area":null},{"id":"2306.10447","title":"Globally Interpretable Graph Learning via Distribution Matching","created":"2023-06-18","cited_by_count":3,"area":null},{"id":"2308.04220","title":"Semantic Interpretation and Validation of Graph Attention-based Explanations for GNN Models","created":"2023-08-08","cited_by_count":3,"area":null},{"id":"2310.09091","title":"Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAI","created":"2023-10-13","cited_by_count":3,"area":null},{"id":"2108.11674","title":"Graph-guided random forest for gene set selection","created":"2021-08-26","cited_by_count":2,"area":null},{"id":"2203.16205","title":"Automatic Identification of Chemical Moieties","created":"2022-03-30","cited_by_count":2,"area":null},{"id":"2307.14849","title":"Counterfactual Explanations for Graph Classification Through the Lenses of Density","created":"2023-07-27","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":"2403.16846","title":"CoDy: Counterfactual Explainers for Dynamic Graphs","created":"2024-03-25","cited_by_count":2,"area":null},{"id":"2406.07642","title":"Generating Human Understandable Explanations for Node Embeddings","created":"2024-06-11","cited_by_count":2,"area":null},{"id":"2505.06123","title":"Wasserstein Distances Made Explainable: Insights Into Dataset Shifts and Transport Phenomena","created":"2025-05-09","cited_by_count":2,"area":null},{"id":"2507.03895","title":"TayFCS: Towards Light Feature Combination Selection for Deep Recommender Systems","created":"2025-07-05","cited_by_count":2,"area":null},{"id":"2508.01048","title":"Explaining GNN Explanations with Edge Gradients","created":"2025-08-01","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":"2511.05064","title":"Order-Level Attention Similarity Across Language Models: A Latent Commonality","created":"2025-11-07","cited_by_count":2,"area":null},{"id":"2603.08328","title":"Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathology","created":"2026-03-09","cited_by_count":2,"area":null},{"id":"2606.00557","title":"Normalized Relevance Measure as a Unifying Framework to Explain Neural Network Latent Structures","created":"2026-05-30","cited_by_count":2,"area":null},{"id":"2205.09934","title":"Towards Explanation for Unsupervised Graph-Level Representation Learning","created":"2022-05-20","cited_by_count":1,"area":null},{"id":"2306.02081","title":"Message-passing selection: Towards interpretable GNNs for graph classification","created":"2023-06-03","cited_by_count":1,"area":null},{"id":"2312.05757","title":"Towards Human-like Perception: Learning Structural Causal Model in Heterogeneous Graph","created":"2023-12-10","cited_by_count":1,"area":null},{"id":"2404.14271","title":"Sparse Explanations of Neural Networks Using Pruned Layer-Wise Relevance Propagation","created":"2024-04-22","cited_by_count":1,"area":null},{"id":"2405.00577","title":"Discovering robust biomarkers of psychiatric disorders from resting-state functional MRI via graph neural networks: A systematic review","created":"2024-05-01","cited_by_count":1,"area":null},{"id":"2407.00849","title":"Towards Understanding Sensitive and Decisive Patterns in Explainable AI: A Case Study of Model Interpretation in Geometric Deep Learning","created":"2024-06-30","cited_by_count":1,"area":null}],"next_cursor":"100"}