{"total":54,"rows":[{"id":"2108.00298","title":"Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks","created":"2021-07-31","cited_by_count":95,"area":null},{"id":"2205.13479","title":"Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations","created":"2022-05-26","cited_by_count":64,"area":null},{"id":"2204.09455","title":"Simplicial Attention Networks","created":"2022-04-20","cited_by_count":41,"area":null},{"id":"2109.12144","title":"Spatial Aggregation and Temporal Convolution Networks for Real-time Kriging","created":"2021-09-24","cited_by_count":27,"area":null},{"id":"2309.02138","title":"Generalized Simplicial Attention Neural Networks","created":"2023-09-05","cited_by_count":27,"area":null},{"id":"2305.18457","title":"Learning Strong Graph Neural Networks with Weak Information","created":"2023-05-29","cited_by_count":25,"area":null},{"id":"2310.04987","title":"Data-centric Graph Learning: A Survey","created":"2023-10-08","cited_by_count":22,"area":null},{"id":"2203.07485","title":"Simplicial Attention Neural Networks","created":"2022-03-14","cited_by_count":21,"area":null},{"id":"2212.02810","title":"Data Imputation with Iterative Graph Reconstruction","created":"2022-12-06","cited_by_count":15,"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":"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":"2501.02191","title":"On LLM-Enhanced Mixed-Type Data Imputation with High-Order Message Passing","created":"2025-01-04","cited_by_count":11,"area":null},{"id":"2109.08357","title":"Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns","created":"2021-09-17","cited_by_count":9,"area":null},{"id":"2211.11596","title":"Forecasting Unobserved Node States with spatio-temporal Graph Neural Networks","created":"2022-11-21","cited_by_count":9,"area":null},{"id":"2106.16057","title":"DAEMA: Denoising Autoencoder with Mask Attention","created":"2021-06-30","cited_by_count":8,"area":null},{"id":"2210.10446","title":"EGG-GAE: scalable graph neural networks for tabular data imputation","created":"2022-10-19","cited_by_count":7,"area":null},{"id":"2311.07344","title":"Missing Value Imputation for Multi-attribute Sensor Data Streams via Message Propagation (Extended Version)","created":"2023-11-13","cited_by_count":7,"area":null},{"id":"2404.01165","title":"LITE: Modeling Environmental Ecosystems with Multimodal Large Language Models","created":"2024-04-01","cited_by_count":7,"area":"mllm"},{"id":"2112.04842","title":"Siamese Attribute-missing Graph Auto-encoder","created":"2021-12-09","cited_by_count":6,"area":null},{"id":"2302.07524","title":"Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network","created":"2023-02-15","cited_by_count":6,"area":null},{"id":"2201.01188","title":"Graph Neural Networks: a bibliometrics overview","created":"2022-01-03","cited_by_count":5,"area":null},{"id":"2201.03202","title":"Differentiable and Scalable Generative Adversarial Models for Data Imputation","created":"2022-01-10","cited_by_count":5,"area":null},{"id":"2208.06573","title":"GEDI: A Graph-based End-to-end Data Imputation Framework","created":"2022-08-13","cited_by_count":5,"area":null},{"id":"2305.06044","title":"Correlation visualization under missing values: a comparison between imputation and direct parameter estimation methods","created":"2023-05-10","cited_by_count":5,"area":null},{"id":"2309.09068","title":"Recovering Missing Node Features with Local Structure-based Embeddings","created":"2023-09-16","cited_by_count":5,"area":null},{"id":"2407.00411","title":"Explainability of Machine Learning Models under Missing Data","created":"2024-06-29","cited_by_count":5,"area":null},{"id":"2305.03741","title":"AmGCL: Feature Imputation of Attribute Missing Graph via Self-supervised Contrastive Learning","created":"2023-05-05","cited_by_count":4,"area":null},{"id":"2102.03450","title":"Wasserstein Graph Neural Networks for Graphs with Missing Attributes","created":"2021-02-06","cited_by_count":3,"area":null},{"id":"2211.00783","title":"Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifiers","created":"2022-11-01","cited_by_count":3,"area":null},{"id":"2409.00904","title":"Multi-scale Temporal Fusion Transformer for Incomplete Vehicle Trajectory Prediction","created":"2024-09-02","cited_by_count":3,"area":null},{"id":"2502.12412","title":"Incomplete Graph Learning: A Comprehensive Survey","created":"2025-02-18","cited_by_count":3,"area":null},{"id":"2106.01708","title":"Semi-supervised Learning with Missing Values Imputation","created":"2021-06-03","cited_by_count":1,"area":null},{"id":"2210.14484","title":"Imputation of missing values in multi-view data","created":"2022-10-26","cited_by_count":1,"area":null},{"id":"2308.09907","title":"Imputing Brain Measurements Across Data Sets via Graph Neural Networks","created":"2023-08-19","cited_by_count":1,"area":null},{"id":"2310.02282","title":"SWMLP: Shared Weight Multilayer Perceptron for Car Trajectory Speed Prediction using Road Topographical Features","created":"2023-10-02","cited_by_count":1,"area":null},{"id":"2406.11934","title":"Bridging Design Gaps: A Parametric Data Completion Approach With Graph Guided Diffusion Models","created":"2024-06-17","cited_by_count":1,"area":"diffusion"},{"id":"2407.20047","title":"Denoising ESG: quantifying data uncertainty from missing data with Machine Learning and prediction intervals","created":"2024-07-29","cited_by_count":1,"area":null},{"id":"2511.22116","title":"IVGAE: Handling Incomplete Heterogeneous Data with a Variational Graph Autoencoder","created":"2025-11-27","cited_by_count":1,"area":null},{"id":"2106.05190","title":"DPER: Efficient Parameter Estimation for Randomly Missing Data","created":"2021-06-06","cited_by_count":0,"area":null},{"id":"2110.04866","title":"CoRGi: Content-Rich Graph Neural Networks with Attention","created":"2021-10-10","cited_by_count":0,"area":null},{"id":"2404.17164","title":"DPGAN: A Dual-Path Generative Adversarial Network for Missing Data Imputation in Graphs","created":"2024-04-26","cited_by_count":0,"area":null},{"id":"2405.13089","title":"SEGAN: semi-supervised learning approach for missing data imputation","created":"2024-05-21","cited_by_count":0,"area":null},{"id":"2410.08794","title":"M$^3$-Impute: Mask-guided Representation Learning for Missing Value Imputation","created":"2024-10-11","cited_by_count":0,"area":null},{"id":"2410.21006","title":"A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networks","created":"2024-10-28","cited_by_count":0,"area":null},{"id":"2411.04907","title":"Enhancing Missing Data Imputation through Combined Bipartite Graph and Complete Directed Graph","created":"2024-11-07","cited_by_count":0,"area":null},{"id":"2501.10151","title":"Topology-Driven Attribute Recovery for Attribute Missing Graph Learning in Social Internet of Things","created":"2025-01-17","cited_by_count":0,"area":null},{"id":"2506.23469","title":"Reconciling Attribute and Structural Anomalies for Improved Graph Anomaly Detection","created":"2025-06-30","cited_by_count":0,"area":null},{"id":"2508.08327","title":"Synthesize, Retrieve, and Propagate: A Unified Predictive Modeling Framework for Relational Databases","created":"2025-08-10","cited_by_count":0,"area":null},{"id":"2509.00651","title":"Missing Data Imputation using Neural Cellular Automata","created":"2025-08-31","cited_by_count":0,"area":null},{"id":"2509.10874","title":"On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals","created":"2025-09-13","cited_by_count":0,"area":null},{"id":"2510.17903","title":"Learning Time-Varying Graphs from Incomplete Graph Signals","created":"2025-10-19","cited_by_count":0,"area":null},{"id":"2511.01196","title":"An Interdisciplinary and Cross-Task Review on Missing Data Imputation","created":"2025-11-03","cited_by_count":0,"area":null},{"id":"2606.31253","title":"MNAR-$k$-means: A $k$-means Clustering for Data Missing Not at Random with Magnitude-Decaying Probability","created":"2026-06-30","cited_by_count":0,"area":null},{"id":"2607.01945","title":"Statistical Properties of $k$-means Clustering for Data Missing Completely at Random","created":"2026-07-02","cited_by_count":0,"area":null}],"next_cursor":null}