{"total":22,"rows":[{"id":"2307.03759","title":"A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection","created":"2023-07-07","cited_by_count":145,"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":"2310.10196","title":"Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook","created":"2023-10-16","cited_by_count":110,"area":null},{"id":"2401.09769","title":"A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions","created":"2024-01-18","cited_by_count":13,"area":null},{"id":"2402.04924","title":"Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching","created":"2024-02-07","cited_by_count":8,"area":null},{"id":"2502.21185","title":"A Survey of Link Prediction in Temporal Networks","created":"2025-02-28","cited_by_count":6,"area":null},{"id":"2404.05774","title":"STMGF: An Effective Spatial-Temporal Multi-Granularity Framework for Traffic Forecasting","created":"2024-04-08","cited_by_count":4,"area":null},{"id":"2407.15320","title":"Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence","created":"2024-07-07","cited_by_count":3,"area":null},{"id":"2410.14109","title":"Improving Graph Neural Networks by Learning Continuous Edge Directions","created":"2024-10-18","cited_by_count":3,"area":null},{"id":"2411.03223","title":"Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation","created":"2024-11-05","cited_by_count":3,"area":null},{"id":"2504.02009","title":"Urban Computing in the Era of Large Language Models","created":"2025-04-02","cited_by_count":2,"area":null},{"id":"2512.13758","title":"Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention","created":"2025-12-15","cited_by_count":2,"area":null},{"id":"2601.12083","title":"Learning to Factorize and Adapt: A Versatile Approach Toward Universal Spatio-Temporal Foundation Models","created":"2026-01-17","cited_by_count":2,"area":null},{"id":"2306.08921","title":"Multi-Temporal Relationship Inference in Urban Areas","created":"2023-06-15","cited_by_count":1,"area":null},{"id":"2410.09307","title":"Graph Neural Alchemist: An innovative fully modular architecture for time series-to-graph classification","created":"2024-10-12","cited_by_count":1,"area":null},{"id":"2411.01070","title":"Explainable Spatio-Temporal GCNNs for Irregular Multivariate Time Series: Architecture and Application to ICU Patient Data","created":"2024-11-01","cited_by_count":1,"area":null},{"id":"2511.05595","title":"FlowNet: Modeling Dynamic Spatio-Temporal Systems via Flow Propagation","created":"2025-11-05","cited_by_count":1,"area":null},{"id":"2603.01657","title":"FreeGNN: Continual Source-Free Graph Neural Network Adaptation for Renewable Energy Forecasting","created":"2026-03-02","cited_by_count":1,"area":null},{"id":"2604.11275","title":"Dynamic Sheaf Diffusion Networks with Adaptive Local Structure for Heterogeneous Spatio-Temporal Graph Learning","created":"2026-04-13","cited_by_count":1,"area":null},{"id":"2512.17352","title":"Adaptive Graph Pruning with Sudden-Events Evaluation for Traffic Prediction using Online Semi-Decentralized ST-GNNs","created":"2025-12-19","cited_by_count":0,"area":null},{"id":"2605.08273","title":"Efficient Prompt Learning for Traffic Forecasting","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2606.17109","title":"Timestamp-Aware Spatio-Temporal Graph Contrastive Learning for Network Intrusion Detection","created":"2026-06-15","cited_by_count":0,"area":null}],"next_cursor":null}