{"total":92,"rows":[{"id":"2204.11423","title":"Trusted Multi-View Classification with Dynamic Evidential Fusion","created":"2022-04-25","cited_by_count":168,"area":null},{"id":"2303.14483","title":"Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey","created":"2023-03-25","cited_by_count":140,"area":null},{"id":"2310.11829","title":"Graph Foundation Models: Concepts, Opportunities and Challenges","created":"2023-10-18","cited_by_count":103,"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":"2305.14535","title":"Uncertainty Quantification over Graph with Conformalized Graph Neural Networks","created":"2023-05-23","cited_by_count":79,"area":null},{"id":"2203.07835","title":"Better Uncertainty Calibration via Proper Scores for Classification and Beyond","created":"2022-03-15","cited_by_count":65,"area":null},{"id":"2210.06391","title":"What Makes Graph Neural Networks Miscalibrated?","created":"2022-10-12","cited_by_count":40,"area":null},{"id":"2201.11349","title":"Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift","created":"2022-01-27","cited_by_count":38,"area":null},{"id":"2212.10888","title":"A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability","created":"2022-12-21","cited_by_count":31,"area":null},{"id":"2308.06714","title":"Learning on Graphs with Out-of-Distribution Nodes","created":"2023-08-13","cited_by_count":29,"area":null},{"id":"2403.07185","title":"Uncertainty in Graph Neural Networks: A Survey","created":"2024-03-11","cited_by_count":23,"area":null},{"id":"2404.15625","title":"Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models","created":"2024-04-24","cited_by_count":22,"area":"diffusion"},{"id":"2205.10014","title":"A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection","created":"2022-05-20","cited_by_count":20,"area":null},{"id":"2501.12868","title":"As Confidence Aligns: Exploring the Effect of AI Confidence on Human Self-confidence in Human-AI Decision Making","created":"2025-01-22","cited_by_count":19,"area":null},{"id":"2306.09612","title":"GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification","created":"2023-06-16","cited_by_count":17,"area":null},{"id":"2210.05959","title":"JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks","created":"2022-10-12","cited_by_count":15,"area":null},{"id":"2506.13083","title":"Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach","created":"2025-06-16","cited_by_count":14,"area":null},{"id":"2309.14673","title":"ALEX: Towards Effective Graph Transfer Learning with Noisy Labels","created":"2023-09-26","cited_by_count":11,"area":null},{"id":"2310.01634","title":"Deep Insights into Noisy Pseudo Labeling on Graph Data","created":"2023-10-02","cited_by_count":11,"area":null},{"id":"2312.08589","title":"Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors","created":"2023-12-14","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":"2302.00873","title":"Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network","created":"2023-02-02","cited_by_count":10,"area":null},{"id":"2312.11858","title":"SimCalib: Graph Neural Network Calibration based on Similarity between Nodes","created":"2023-12-19","cited_by_count":10,"area":null},{"id":"2406.18763","title":"Conformalized Link Prediction on Graph Neural Networks","created":"2024-06-26","cited_by_count":9,"area":null},{"id":"2401.03350","title":"Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks","created":"2024-01-07","cited_by_count":8,"area":null},{"id":"2211.16466","title":"Birds of a Feather Trust Together: Knowing When to Trust a Classifier via Adaptive Neighborhood Aggregation","created":"2022-11-29","cited_by_count":7,"area":null},{"id":"2306.00015","title":"GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks","created":"2023-05-30","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":"2405.14303","title":"Similarity-Navigated Conformal Prediction for Graph Neural Networks","created":"2024-05-23","cited_by_count":7,"area":null},{"id":"2210.15575","title":"A Graph Is More Than Its Nodes: Towards Structured Uncertainty-Aware Learning on Graphs","created":"2022-10-27","cited_by_count":6,"area":null},{"id":"2307.09858","title":"Towards Reliable Rare Category Analysis on Graphs via Individual Calibration","created":"2023-07-19","cited_by_count":6,"area":null},{"id":"2402.07632","title":"Understanding the Effects of Miscalibrated AI Confidence on User Trust, Reliance, and Decision Efficacy","created":"2024-02-12","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":"2403.02723","title":"Minimum Topology Attacks for Graph Neural Networks","created":"2024-03-05","cited_by_count":6,"area":null},{"id":"2407.10784","title":"AdapTable: Test-Time Adaptation for Tabular Data via Shift-Aware Uncertainty Calibrator and Label Distribution Handler","created":"2024-07-15","cited_by_count":6,"area":null},{"id":"2501.02767","title":"Enhancing Trustworthiness of Graph Neural Networks with Rank-Based Conformal Training","created":"2025-01-06","cited_by_count":6,"area":null},{"id":"2505.11335","title":"Understanding and Mitigating Under-Confidence in GNNs from the Final Layer","created":"2025-05-16","cited_by_count":6,"area":null},{"id":"2310.12565","title":"Open-World Lifelong Graph Learning","created":"2023-10-19","cited_by_count":5,"area":null},{"id":"2311.05795","title":"Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization","created":"2023-11-10","cited_by_count":5,"area":null},{"id":"2303.14898","title":"Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning","created":"2023-03-27","cited_by_count":4,"area":"distill"},{"id":"2304.02277","title":"Rethinking the Trigger-injecting Position in Graph Backdoor Attack","created":"2023-04-05","cited_by_count":4,"area":null},{"id":"2305.05912","title":"A Hybrid of Generative and Discriminative Models Based on the Gaussian-coupled Softmax Layer","created":"2023-05-10","cited_by_count":4,"area":null},{"id":"2306.07036","title":"Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision","created":"2023-06-12","cited_by_count":4,"area":null},{"id":"2310.11479","title":"On the Temperature of Bayesian Graph Neural Networks for Conformal Prediction","created":"2023-10-17","cited_by_count":4,"area":null},{"id":"2312.04692","title":"Diffence: Fencing Membership Privacy With Diffusion Models","created":"2023-12-07","cited_by_count":4,"area":"diffusion"},{"id":"2401.14876","title":"Cross-Space Adaptive Filter: Integrating Graph Topology and Node Attributes for Alleviating the Over-smoothing Problem","created":"2024-01-26","cited_by_count":4,"area":null},{"id":"2404.14642","title":"Uncertainty Quantification on Graph Learning: A Survey","created":"2024-04-23","cited_by_count":4,"area":null},{"id":"2410.09570","title":"GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks","created":"2024-10-12","cited_by_count":4,"area":null},{"id":"2502.12618","title":"Uncertainty-Aware Graph Structure Learning","created":"2025-02-18","cited_by_count":4,"area":null},{"id":"2602.06733","title":"Pairwise is Not Enough: Hypergraph Neural Networks for Multi-Agent Pathfinding","created":"2026-02-06","cited_by_count":4,"area":null},{"id":"2403.04605","title":"In-n-Out: Calibrating Graph Neural Networks for Link Prediction","created":"2024-03-07","cited_by_count":3,"area":null},{"id":"2410.07014","title":"Optimizing Estimators of Squared Calibration Errors in Classification","created":"2024-10-09","cited_by_count":3,"area":null},{"id":"2410.15241","title":"Conditional Uncertainty Quantification for Tensorized Topological Neural Networks","created":"2024-10-20","cited_by_count":3,"area":null},{"id":"2503.19455","title":"Data-centric Federated Graph Learning with Large Language Models","created":"2025-03-25","cited_by_count":3,"area":null},{"id":"2505.01766","title":"Multimodal Graph Representation Learning for Robust Surgical Workflow Recognition with Adversarial Feature Disentanglement","created":"2025-05-03","cited_by_count":3,"area":null},{"id":"2605.11987","title":"Random-Set Graph Neural Networks","created":"2026-05-12","cited_by_count":3,"area":null},{"id":"2301.08918","title":"Improving Signed Propagation for Graph Neural Networks in Multi-Class Environments","created":"2023-01-21","cited_by_count":2,"area":null},{"id":"2302.05137","title":"Realistic Conversational Question Answering with Answer Selection based on Calibrated Confidence and Uncertainty Measurement","created":"2023-02-10","cited_by_count":2,"area":null},{"id":"2405.20986","title":"Predictive Uncertainty Quantification for Bird's Eye View Segmentation: A Benchmark and Novel Loss Function","created":"2024-05-31","cited_by_count":2,"area":null},{"id":"2407.17787","title":"HC-GST: Heterophily-aware Distribution Consistency based Graph Self-training","created":"2024-07-25","cited_by_count":2,"area":null},{"id":"2410.09348","title":"BANGS: Game-Theoretic Node Selection for Graph Self-Training","created":"2024-10-12","cited_by_count":2,"area":null},{"id":"2506.07854","title":"Residual Reweighted Conformal Prediction for Graph Neural Networks","created":"2025-06-09","cited_by_count":2,"area":null},{"id":"2506.23782","title":"WATS: Calibrating Graph Neural Networks with Wavelet-Aware Temperature Scaling","created":"2025-06-30","cited_by_count":2,"area":null},{"id":"2607.25273","title":"HeAD-CP: Heterophily-Aware Diffused Conformal Prediction Sets for Graph Neural Networks","created":"2026-07-28","cited_by_count":2,"area":null},{"id":"2211.16631","title":"Every Node Counts: Improving the Training of Graph Neural Networks on Node Classification","created":"2022-11-29","cited_by_count":1,"area":null},{"id":"2310.06380","title":"CAST: Cluster-Aware Self-Training for Tabular Data via Reliable Confidence","created":"2023-10-10","cited_by_count":1,"area":null},{"id":"2409.06998","title":"Mixture of Scope Experts at Test: Generalizing Deeper Graph Neural Networks with Shallow Variants","created":"2024-09-11","cited_by_count":1,"area":null},{"id":"2409.18332","title":"Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphs","created":"2024-09-26","cited_by_count":1,"area":null},{"id":"2412.14223","title":"Towards Precise Prediction Uncertainty in GNNs: Refining GNNs with Topology-grouping Strategy","created":"2024-12-18","cited_by_count":1,"area":null},{"id":"2503.18235","title":"Enhance GNNs with Reliable Confidence Estimation via Adversarial Calibration Learning","created":"2025-03-23","cited_by_count":1,"area":null},{"id":"2504.01533","title":"LightDefense: A Lightweight Uncertainty-Driven Defense against Jailbreaks via Shifted Token Distribution","created":"2025-04-02","cited_by_count":1,"area":"safety"},{"id":"2205.03753","title":"Select and Calibrate the Low-confidence: Dual-Channel Consistency based Graph Convolutional Networks","created":"2022-05-08","cited_by_count":0,"area":null},{"id":"2210.11616","title":"Generalized Reciprocal Perspective","created":"2022-10-20","cited_by_count":0,"area":null},{"id":"2211.15081","title":"Mitigating Overfitting in Graph Neural Networks via Feature and Hyperplane Perturbation","created":"2022-11-28","cited_by_count":0,"area":null},{"id":"2309.10976","title":"Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks","created":"2023-09-20","cited_by_count":0,"area":null},{"id":"2404.09585","title":"Pseudo-label Learning with Calibrated Confidence Using an Energy-based Model","created":"2024-04-15","cited_by_count":0,"area":null},{"id":"2408.13825","title":"RoCP-GNN: Robust Conformal Prediction for Graph Neural Networks in Node-Classification","created":"2024-08-25","cited_by_count":0,"area":null},{"id":"2502.00716","title":"UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification","created":"2025-02-02","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":"2504.02248","title":"CRC-SGAD: Conformal Risk Control for Supervised Graph Anomaly Detection","created":"2025-04-03","cited_by_count":0,"area":null},{"id":"2505.16466","title":"Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods","created":"2025-05-22","cited_by_count":0,"area":null},{"id":"2508.08769","title":"Differentiated Information Mining: A Semi-supervised Learning Framework for GNNs","created":"2025-08-12","cited_by_count":0,"area":null},{"id":"2508.17097","title":"Two Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural Process","created":"2025-08-23","cited_by_count":0,"area":null},{"id":"2602.19392","title":"Spiking Graph Predictive Coding for Reliable OOD Generalization","created":"2026-02-22","cited_by_count":0,"area":null},{"id":"2605.06814","title":"Model-to-Data Distillation for Graph Neural Networks","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.08074","title":"GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphs","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2606.08467","title":"The Confidence Trap: Calibration Attacks for Graph Neural Networks","created":"2026-06-07","cited_by_count":0,"area":null},{"id":"2607.10804","title":"When does distribution shift break graph neural networks calibration?","created":"2026-07-12","cited_by_count":0,"area":null},{"id":"2609.02056","title":"HyGRAIL: Cost-Aware and Evidence-Grounded Scientific Hypothesis Discovery over Knowledge Graphs","created":"2026-09-02","cited_by_count":0,"area":null},{"id":"2609.09801","title":"Morphological Decoupling-Based Skeletal Classification for Clinical Assessment of Malocclusion","created":"2026-09-09","cited_by_count":0,"area":null},{"id":"2609.13655","title":"Online Bayesian Node Classification on Inductive Graphs under Distribution Shift","created":"2026-09-12","cited_by_count":0,"area":null},{"id":"2609.32426","title":"HoTS: Homophily-Aware Temperature Scaling for Graph Neural Network Calibration","created":"2026-09-26","cited_by_count":0,"area":null}],"next_cursor":null}