mlsanityData to 7 Oct 2026

Kriging Convolutional Networks

arXiv 2306.09463 ↗First posted 15 Jun 2023cs.LG cs.AIcited by 30

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Abstract (arXiv)

Spatial interpolation is a class of estimation problems where locations with known values are used to estimate values at other locations, with an emphasis on harnessing spatial locality and trends. Traditional Kriging methods have strong Gaussian assumptions, and as a result, often fail to capture complexities within the data. Inspired by the recent progress of graph neural networks, we introduce Kriging Convolutional Networks (KCN), a method of combining the advantages of Graph Convolutional Networks (GCN) and Kriging. Compared to standard GCNs, KCNs make direct use of neighboring observations when generating predictions. KCNs also contain the Kriging method as a specific configuration. We further improve the model's performance by adding attention. Empirically, we show that this model outperforms GCNs and Kriging in several applications. The implementation of KCN using PyTorch is publicized at the GitHub repository: https://github.com/tufts-ml/kcn-torch.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). Builds on 11 of its 27 references that are in the map. First posted on arXiv 15 Jun 2023.

Builds on

11 of 27 references.

References · 11

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2017Inductive Representation Learning on Large Graphscited by 6,284
  5. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  6. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  7. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  8. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  9. 2015Diffusion-Convolutional Neural Networkscited by 268
  10. 2015ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and Rcited by 171
  11. 2018Hyperparameters and Tuning Strategies for Random Forestcited by 83

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 9; 2025: 13; 2026: 5.

30 papers.

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  1. 2023Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learningcited by 20
  2. 2023KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategycited by 19
  3. 2024Prompt-Based Spatio-Temporal Graph Transfer Learningcited by 9
  4. 2024Graph-based Virtual Sensing from Sparse and Partial Multivariate Observationscited by 8
  5. 2024Spatial-temporal Forecasting for Regions without Observationscited by 6
  6. 2024Joint Estimation and Prediction of City-wide Delivery Demand: A Large Language Model Empowered Graph-based Learning Approachcited by 6
  7. 2024Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical Learningcited by 5
  8. 2023SSIN: Self-Supervised Learning for Rainfall Spatial Interpolationcited by 4
  9. 2025STA-GANN: A Valid and Generalizable Spatio-Temporal Kriging Approachcited by 4
  10. 2025MoGERNN: An Inductive Traffic Predictor for Unobserved Locationscited by 2
  11. 2025Spatio-Temporal Graph Neural Network for Urban Spaces: Interpolating Citywide Traffic Volumecited by 2
  12. 2025DeepTopoNet: A Framework for Subglacial Topography Estimation on the Greenland Ice Sheetscited by 2
18 more
  1. 2024Inductive Spatial Temporal Prediction Under Data Drift with Informative Graph Neural Networkcited by 1
  2. 2024Positional Encoder Graph Quantile Neural Networks for Geographic Datacited by 1
  3. 2025Urban Incident Prediction with Graph Neural Networks: Integrating Government Ratings and Crowdsourced Reportscited by 1
  4. 2025Improving Greenland Bed Topography Mapping with Uncertainty-Aware Graph Learning on Sparse Radar Datacited by 1
  5. 2025AnchorGK: Anchor-based Incremental and Stratified Graph Learning Framework for Inductive Spatio-Temporal Krigingcited by 1
  6. 2024Spatio-Temporal Partial Sensing Forecast for Long-term Trafficcited by 0
  7. 2024A Survey and Benchmarking of Spatial-Temporal Traffic Data Imputation Modelscited by 0
  8. 2025Inductive Spatio-Temporal Kriging with Physics-Guided Increment Training Strategy for Air Quality Inferencecited by 0
  9. 2025RelMap: Reliable Spatiotemporal Sensor Data Visualization via Imputative Spatial Interpolationcited by 0
  10. 2025Leakage-Free Evaluation and Distribution-Robust Spatio-Temporal Graph Learning for Inductive Krigingcited by 0
  11. 2025Spatio-temporal Multivariate Time Series Forecast with Chosen Variablescited by 0
  12. 2025Learning Subglacial Bed Topography from Sparse Radar with Physics-Guided Residualscited by 0
  13. 2025A Hybrid Inductive-Transductive Network for Traffic Flow Imputation on Unsampled Locationscited by 0
  14. 2026A Geolocation-Aware Multimodal Approach for Ecological Predictioncited by 0
  15. 2026PlugSI: Plug-and-Play Test-Time Graph Adaptation for Spatial Interpolationcited by 0
  16. 2026Uniform Inductive Spatio-Temporal Krigingcited by 0
  17. 2026AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networkscited by 0
  18. 2026Zhinv: Real-time hub-height wind field reconstruction using only local sparse observationscited by 0