{"total":61,"rows":[{"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":"2202.08871","title":"Graph Data Augmentation for Graph Machine Learning: A Survey","created":"2022-02-17","cited_by_count":62,"area":null},{"id":"2303.01263","title":"Unnoticeable Backdoor Attacks on Graph Neural Networks","created":"2023-02-11","cited_by_count":45,"area":null},{"id":"2310.01267","title":"Cooperative Graph Neural Networks","created":"2023-10-02","cited_by_count":36,"area":null},{"id":"2402.15183","title":"GraphEdit: Large Language Models for Graph Structure Learning","created":"2024-02-23","cited_by_count":33,"area":null},{"id":"2211.06614","title":"Robust Training of Graph Neural Networks via Noise Governance","created":"2022-11-12","cited_by_count":29,"area":null},{"id":"2202.11245","title":"Exploring Edge Disentanglement for Node Classification","created":"2022-02-23","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":"2306.08194","title":"Learning on Graphs under Label Noise","created":"2023-06-14","cited_by_count":22,"area":null},{"id":"2405.10757","title":"Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective","created":"2024-05-17","cited_by_count":22,"area":null},{"id":"2307.07832","title":"MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation","created":"2023-07-15","cited_by_count":18,"area":null},{"id":"2402.08907","title":"Subgraph Pooling: Tackling Negative Transfer on Graphs","created":"2024-02-14","cited_by_count":17,"area":null},{"id":"2309.10979","title":"Towards Data-centric Graph Machine Learning: Review and Outlook","created":"2023-09-20","cited_by_count":15,"area":null},{"id":"2201.12741","title":"GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks","created":"2022-01-30","cited_by_count":14,"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":"2309.02025","title":"RDGSL: Dynamic Graph Representation Learning with Structure Learning","created":"2023-09-05","cited_by_count":13,"area":null},{"id":"2401.02143","title":"Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions","created":"2024-01-04","cited_by_count":13,"area":null},{"id":"2208.07012","title":"MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature Distribution","created":"2022-08-15","cited_by_count":11,"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":"2202.07114","title":"Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack","created":"2022-02-15","cited_by_count":10,"area":null},{"id":"2309.00894","title":"Regularly Truncated M-estimators for Learning with Noisy Labels","created":"2023-09-02","cited_by_count":8,"area":null},{"id":"2402.11837","title":"Self-Guided Robust Graph Structure Refinement","created":"2024-02-19","cited_by_count":8,"area":null},{"id":"2405.13902","title":"LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework","created":"2024-05-22","cited_by_count":8,"area":null},{"id":"2407.15351","title":"LLMExplainer: Large Language Model based Bayesian Inference for Graph Explanation Generation","created":"2024-07-22","cited_by_count":8,"area":null},{"id":"2312.05526","title":"Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection","created":"2023-12-09","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":"2505.16284","title":"Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse","created":"2025-05-22","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":"2306.13854","title":"Similarity Preserving Adversarial Graph Contrastive Learning","created":"2023-06-24","cited_by_count":6,"area":null},{"id":"2403.01942","title":"Mitigating Label Noise on Graph via Topological Sample Selection","created":"2024-03-04","cited_by_count":6,"area":null},{"id":"2406.13201","title":"Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing Approach","created":"2024-06-19","cited_by_count":6,"area":null},{"id":"2411.17063","title":"Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning","created":"2024-11-26","cited_by_count":6,"area":null},{"id":"2307.11341","title":"OpenGDA: Graph Domain Adaptation Benchmark for Cross-network Learning","created":"2023-07-21","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":"2311.00322","title":"Robust Graph Clustering via Meta Weighting for Noisy Graphs","created":"2023-11-01","cited_by_count":5,"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":"2407.19420","title":"UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification Tasks","created":"2024-07-28","cited_by_count":3,"area":null},{"id":"2411.12775","title":"Revisiting Fake News Detection: Towards Temporality-aware Evaluation by Leveraging Engagement Earliness","created":"2024-11-19","cited_by_count":3,"area":null},{"id":"2407.13158","title":"HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning","created":"2024-07-18","cited_by_count":2,"area":null},{"id":"2501.04161","title":"KGIF: Optimizing Relation-Aware Recommendations with Knowledge Graph Information Fusion","created":"2025-01-07","cited_by_count":2,"area":null},{"id":"2310.16401","title":"Graph Neural Networks with a Distribution of Parametrized Graphs","created":"2023-10-25","cited_by_count":1,"area":null},{"id":"2402.02321","title":"Active Learning for Graphs with Noisy Structures","created":"2024-02-04","cited_by_count":1,"area":null},{"id":"2402.09600","title":"Graph Contrastive Learning with Low-Rank Regularization and Low-Rank Attention for Noisy Node Classification","created":"2024-02-14","cited_by_count":1,"area":null},{"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":"2601.17469","title":"Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction","created":"2026-01-24","cited_by_count":1,"area":null},{"id":"2412.08419","title":"Energy Guided smoothness to improve Robustness in Graph Classification","created":"2024-12-11","cited_by_count":0,"area":null},{"id":"2502.16157","title":"Advanced Text Analytics -- Graph Neural Network for Fake News Detection in Social Media","created":"2025-02-22","cited_by_count":0,"area":null},{"id":"2502.19670","title":"Training Robust Graph Neural Networks by Modeling Noise Dependencies","created":"2025-02-27","cited_by_count":0,"area":null},{"id":"2503.12157","title":"Weighted Graph Structure Learning with Attention Denoising for Node Classification","created":"2025-03-15","cited_by_count":0,"area":null},{"id":"2503.17842","title":"Adapt, Agree, Aggregate: Semi-Supervised Ensemble Labeling for Graph Convolutional Networks","created":"2025-03-22","cited_by_count":0,"area":null},{"id":"2506.11611","title":"KCES: Training-Free Defense for Robust Graph Neural Networks via Kernel Complexity","created":"2025-06-13","cited_by_count":0,"area":null},{"id":"2507.05540","title":"Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge","created":"2025-07-07","cited_by_count":0,"area":null},{"id":"2508.17531","title":"Gumbel-MPNN: Graph Rewiring with Gumbel-Softmax","created":"2025-08-24","cited_by_count":0,"area":null},{"id":"2601.17449","title":"DREAM: Dual-Standard Semantic Homogeneity with Dynamic Optimization for Graph Learning with Label Noise","created":"2026-01-24","cited_by_count":0,"area":null},{"id":"2603.05004","title":"Poisoning the Inner Prediction Logic of Graph Neural Networks for Clean-Label Backdoor Attacks","created":"2026-03-05","cited_by_count":0,"area":null},{"id":"2606.03462","title":"Topology-Aware Gaussian Graph Repair for Robust Graph Neural Networks","created":"2026-06-02","cited_by_count":0,"area":null},{"id":"2606.11640","title":"TAROT: Task-Adaptive Refinement of LLM-prior Graphs for Few-shot Tabular Learning","created":"2026-06-10","cited_by_count":0,"area":null},{"id":"2608.15768","title":"Temporal Graph Prototype-conditioned Conformal Prediction for Fraud Detection","created":"2026-08-16","cited_by_count":0,"area":null},{"id":"2608.22920","title":"Beyond Observed Auxiliary Relations: Environment-Conditioned Modeling for Multi-Behavior Recommendation","created":"2026-08-24","cited_by_count":0,"area":null},{"id":"2609.09698","title":"Kernel-Complexity Edge Sanitization for Training-Free Defense against Structural Graph Attacks","created":"2026-09-09","cited_by_count":0,"area":null}],"next_cursor":null}