{"total":118,"rows":[{"id":"2205.07424","title":"Trustworthy Graph Neural Networks: Aspects, Methods and Trends","created":"2022-05-16","cited_by_count":89,"area":null},{"id":"2302.02591","title":"Generative Diffusion Models on Graphs: Methods and Applications","created":"2023-02-06","cited_by_count":81,"area":"diffusion"},{"id":"2404.01349","title":"Fairness in Large Language Models: A Taxonomic Survey","created":"2024-03-31","cited_by_count":58,"area":null},{"id":"2403.04468","title":"A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges","created":"2024-03-07","cited_by_count":54,"area":null},{"id":"2303.01263","title":"Unnoticeable Backdoor Attacks on Graph Neural Networks","created":"2023-02-11","cited_by_count":45,"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":"2208.10609","title":"Global Concept-Based Interpretability for Graph Neural Networks via Neuron Analysis","created":"2022-08-22","cited_by_count":32,"area":null},{"id":"2209.10117","title":"A Comprehensive Survey on Trustworthy Recommender Systems","created":"2022-09-21","cited_by_count":32,"area":null},{"id":"2402.04013","title":"Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses","created":"2024-02-06","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":"2307.04937","title":"Towards Fair Graph Neural Networks via Graph Counterfactual","created":"2023-07-10","cited_by_count":23,"area":null},{"id":"2304.01391","title":"Counterfactual Learning on Graphs: A Survey","created":"2023-04-03","cited_by_count":22,"area":null},{"id":"2308.16375","title":"A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications","created":"2023-08-31","cited_by_count":22,"area":null},{"id":"2407.19398","title":"IDEA: A Flexible Framework of Certified Unlearning for Graph Neural Networks","created":"2024-07-28","cited_by_count":22,"area":null},{"id":"2310.13007","title":"A Critical Survey on Fairness Benefits of Explainable AI","created":"2023-10-15","cited_by_count":16,"area":null},{"id":"2306.08604","title":"A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy","created":"2023-06-14","cited_by_count":14,"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":"2210.08195","title":"HP-GMN: Graph Memory Networks for Heterophilous Graphs","created":"2022-10-15","cited_by_count":13,"area":null},{"id":"2212.08689","title":"TopoImb: Toward Topology-level Imbalance in Learning from Graphs","created":"2022-12-16","cited_by_count":12,"area":null},{"id":"2312.08852","title":"ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance","created":"2023-12-13","cited_by_count":11,"area":null},{"id":"2405.11034","title":"Safety in Graph Machine Learning: Threats and Safeguards","created":"2024-05-17","cited_by_count":11,"area":null},{"id":"2410.00049","title":"Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph","created":"2024-09-28","cited_by_count":10,"area":null},{"id":"2310.19321","title":"D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion","created":"2023-10-30","cited_by_count":9,"area":"diffusion"},{"id":"2311.01196","title":"Combating Bilateral Edge Noise for Robust Link Prediction","created":"2023-11-02","cited_by_count":9,"area":null},{"id":"2401.04331","title":"Coupling Graph Neural Networks with Fractional Order Continuous Dynamics: A Robustness Study","created":"2024-01-09","cited_by_count":9,"area":null},{"id":"2302.14353","title":"A semantic backdoor attack against Graph Convolutional Networks","created":"2023-02-28","cited_by_count":8,"area":null},{"id":"2310.16421","title":"Graph Agent: Explicit Reasoning Agent for Graphs","created":"2023-10-25","cited_by_count":8,"area":"lr"},{"id":"2401.02086","title":"View-based Explanations for Graph Neural Networks","created":"2024-01-04","cited_by_count":8,"area":null},{"id":"2305.10673","title":"Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs","created":"2023-05-18","cited_by_count":7,"area":null},{"id":"2306.11132","title":"Fairness-aware Message Passing for Graph Neural Networks","created":"2023-06-19","cited_by_count":7,"area":null},{"id":"2401.10394","title":"Distribution Consistency based Self-Training for Graph Neural Networks with Sparse Labels","created":"2024-01-18","cited_by_count":7,"area":null},{"id":"2403.06017","title":"Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark","created":"2024-03-09","cited_by_count":7,"area":null},{"id":"2406.09836","title":"Robustness Inspired Graph Backdoor Defense","created":"2024-06-14","cited_by_count":7,"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":"2301.05860","title":"State of the Art and Potentialities of Graph-level Learning","created":"2023-01-14","cited_by_count":6,"area":null},{"id":"2404.00673","title":"A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures","created":"2024-03-31","cited_by_count":6,"area":null},{"id":"2409.08658","title":"Promoting Fairness in Link Prediction with Graph Enhancement","created":"2024-09-13","cited_by_count":6,"area":null},{"id":"2207.10896","title":"Privacy and Transparency in Graph Machine Learning: A Unified Perspective","created":"2022-07-22","cited_by_count":5,"area":null},{"id":"2209.09688","title":"Sparse Vicious Attacks on Graph Neural Networks","created":"2022-09-20","cited_by_count":5,"area":null},{"id":"2308.01469","title":"VertexSerum: Poisoning Graph Neural Networks for Link Inference","created":"2023-08-02","cited_by_count":5,"area":null},{"id":"2310.03312","title":"Certifiably Robust Graph Contrastive Learning","created":"2023-10-05","cited_by_count":5,"area":null},{"id":"2506.19862","title":"DualEquiNet: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules","created":"2025-06-10","cited_by_count":5,"area":null},{"id":"2508.17222","title":"Exposing Privacy Risks in Graph Retrieval-Augmented Generation","created":"2025-08-24","cited_by_count":5,"area":"rag"},{"id":"2304.02277","title":"Rethinking the Trigger-injecting Position in Graph Backdoor Attack","created":"2023-04-05","cited_by_count":4,"area":null},{"id":"2309.06584","title":"Self-explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction","created":"2023-09-12","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":"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":"2402.08824","title":"Disambiguated Node Classification with Graph Neural Networks","created":"2024-02-13","cited_by_count":4,"area":null},{"id":"2406.10425","title":"Multi-source Unsupervised Domain Adaptation on Graphs with Transferability Modeling","created":"2024-06-14","cited_by_count":4,"area":null},{"id":"2505.02566","title":"Robustness questions the interpretability of graph neural networks: what to do?","created":"2025-05-05","cited_by_count":4,"area":null},{"id":"2506.00808","title":"Unlearning Inversion Attacks for Graph Neural Networks","created":"2025-06-01","cited_by_count":4,"area":null},{"id":"2601.09394","title":"FairGE: Fairness-Aware Graph Encoding in Incomplete Social Networks","created":"2026-01-14","cited_by_count":4,"area":null},{"id":"2305.15822","title":"Towards Label Position Bias in Graph Neural Networks","created":"2023-05-25","cited_by_count":3,"area":null},{"id":"2307.13548","title":"Node Injection Link Stealing Attack","created":"2023-07-25","cited_by_count":3,"area":null},{"id":"2308.09596","title":"Disparity, Inequality, and Accuracy Tradeoffs in Graph Neural Networks for Node Classification","created":"2023-08-18","cited_by_count":3,"area":null},{"id":"2402.04435","title":"PreGIP: Watermarking the Pretraining of Graph Neural Networks for Deep Intellectual Property Protection","created":"2024-02-06","cited_by_count":3,"area":null},{"id":"2411.11197","title":"Stealing Training Graphs from Graph Neural Networks","created":"2024-11-17","cited_by_count":3,"area":"diffusion"},{"id":"2412.15735","title":"Prompt-based Unifying Inference Attack on Graph Neural Networks","created":"2024-12-20","cited_by_count":3,"area":null},{"id":"2502.06914","title":"UniZyme: A Unified Protein Cleavage Site Predictor Enhanced with Enzyme Active-Site Knowledge","created":"2025-02-10","cited_by_count":3,"area":null},{"id":"2503.19382","title":"Causal invariant geographic network representations with feature and structural distribution shifts","created":"2025-03-25","cited_by_count":3,"area":null},{"id":"2505.09702","title":"Enabling Group Fairness in Graph Unlearning via Bi-level Debiasing","created":"2025-05-14","cited_by_count":3,"area":"safety"},{"id":"2508.19641","title":"Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses","created":"2025-08-27","cited_by_count":3,"area":null},{"id":"2602.07708","title":"Quantifying Explanation Quality in Graph Neural Networks using Out-of-Distribution Generalization","created":"2026-02-07","cited_by_count":3,"area":null},{"id":"2210.11730","title":"Privacy-Preserved Neural Graph Similarity Learning","created":"2022-10-21","cited_by_count":2,"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":"2310.14527","title":"Marginal Nodes Matter: Towards Structure Fairness in Graphs","created":"2023-10-23","cited_by_count":2,"area":null},{"id":"2402.12937","title":"GRAPHGINI: Fostering Individual and Group Fairness in Graph Neural Networks","created":"2024-02-20","cited_by_count":2,"area":null},{"id":"2405.02583","title":"Explainable Interface for Human-Autonomy Teaming: A Survey","created":"2024-05-04","cited_by_count":2,"area":null},{"id":"2407.12112","title":"A Benchmark for Fairness-Aware Graph Learning","created":"2024-07-16","cited_by_count":2,"area":null},{"id":"2503.22998","title":"AuditVotes: Elevating Provable Defense for GNNs with Efficient Augmentation and Conditional Smoothing","created":"2025-03-29","cited_by_count":2,"area":null},{"id":"2504.21296","title":"Fairness in Augmented Graph Learning: A Survey","created":"2025-04-30","cited_by_count":2,"area":null},{"id":"2506.00437","title":"Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks","created":"2025-05-31","cited_by_count":2,"area":null},{"id":"2511.06568","title":"Breaking the Dyadic Barrier: Rethinking Fairness in Link Prediction Beyond Demographic Parity","created":"2025-11-09","cited_by_count":2,"area":null},{"id":"2307.06963","title":"Is Task-Agnostic Explainable AI a Myth?","created":"2023-07-13","cited_by_count":1,"area":null},{"id":"2309.01335","title":"In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems","created":"2023-09-04","cited_by_count":1,"area":null},{"id":"2311.11473","title":"CSGNN: Conquering Noisy Node labels via Dynamic Class-wise Selection","created":"2023-11-20","cited_by_count":1,"area":null},{"id":"2402.04033","title":"On provable privacy vulnerabilities of graph representations","created":"2024-02-06","cited_by_count":1,"area":null},{"id":"2403.17647","title":"Intrinsic Subgraph Generation for Interpretable Graph based Visual Question Answering","created":"2024-03-26","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":"2405.13832","title":"Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Review","created":"2024-05-22","cited_by_count":1,"area":null},{"id":"2406.13920","title":"Understanding the Robustness of Graph Neural Networks against Adversarial Attacks","created":"2024-06-20","cited_by_count":1,"area":null},{"id":"2410.21163","title":"Resilience in Knowledge Graph Embeddings","created":"2024-10-28","cited_by_count":1,"area":null},{"id":"2412.00382","title":"Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation","created":"2024-11-30","cited_by_count":1,"area":"distill"},{"id":"2502.09271","title":"LiSA: Leveraging Link Recommender to Attack Graph Neural Networks via Subgraph Injection","created":"2025-02-13","cited_by_count":1,"area":null},{"id":"2503.02959","title":"Node-level Contrastive Unlearning on Graph Neural Networks","created":"2025-03-04","cited_by_count":1,"area":null},{"id":"2504.21206","title":"FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs","created":"2025-04-29","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":"2506.03087","title":"Do Explanations Increase the Risk of Decision Logic Leakage? Explanation-Guided Stealing of Graph Models","created":"2025-06-03","cited_by_count":1,"area":null},{"id":"2506.08602","title":"WGLE:Backdoor-free and Multi-bit Black-box Watermarking for Graph Neural Networks","created":"2025-06-10","cited_by_count":1,"area":null},{"id":"2506.12382","title":"Exploring the Secondary Risks of Large Language Models","created":"2025-06-14","cited_by_count":1,"area":"alignment"},{"id":"2512.06154","title":"Learning Invariant Graph Representations Through Redundant Information","created":"2025-12-05","cited_by_count":1,"area":null},{"id":"2601.17130","title":"Impact of Graph Structure on Membership-Inference Risk for Graph Neural Networks","created":"2026-01-23","cited_by_count":1,"area":null},{"id":"2602.02512","title":"Efficient Edge Rewiring Strategies for Enhancing PageRank Fairness","created":"2026-01-23","cited_by_count":1,"area":null},{"id":"2603.21296","title":"DeepXplain: XAI-Guided Autonomous Defense Against Multi-Stage APT Campaigns","created":"2026-03-22","cited_by_count":1,"area":"deeprl"},{"id":"2608.12083","title":"Faithful, Sufficient and Understandable: Rethinking Graph Counterfactual Explanations via Discrete Diffusion Inversion","created":"2026-08-12","cited_by_count":1,"area":"diffusion"},{"id":"2311.01205","title":"Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent","created":"2023-11-02","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":"2411.06881","title":"WassFFed: Wasserstein Fair Federated Learning","created":"2024-11-11","cited_by_count":0,"area":null},{"id":"2503.12156","title":"Efficient and Privacy-Preserved Link Prediction via Condensed Graphs","created":"2025-03-15","cited_by_count":0,"area":null},{"id":"2503.20630","title":"$\\beta$-GNN: A Robust Ensemble Approach Against Graph Structure Perturbation","created":"2025-03-26","cited_by_count":0,"area":null}],"next_cursor":"100"}