mlsanityData to 14 Oct 2026

Positional Encoder Graph Neural Networks for Geographic Data

arXiv 2111.10144 ↗First posted 19 Nov 2021cs.LG cs.CVcited by 20

Not on the map. Why?

Abstract (arXiv)

Graph neural networks (GNNs) provide a powerful and scalable solution for modeling continuous spatial data. However, they often rely on Euclidean distances to construct the input graphs. This assumption can be improbable in many real-world settings, where the spatial structure is more complex and explicitly non-Euclidean (e.g., road networks). Here, we propose PE-GNN, a new framework that incorporates spatial context and correlation explicitly into the models. Building on recent advances in geospatial auxiliary task learning and semantic spatial embeddings, our proposed method (1) learns a context-aware vector encoding of the geographic coordinates and (2) predicts spatial autocorrelation in the data in parallel with the main task. On spatial interpolation and regression tasks, we show the effectiveness of our approach, improving performance over different state-of-the-art GNN approaches. We observe that our approach not only vastly improves over the GNN baselines, but can match Gaussian processes, the most commonly utilized method for spatial interpolation problems.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). Builds on 20 of its 41 references that are in the map. First posted on arXiv 19 Nov 2021.

Builds on

20 of 41 references.

References · 20

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  4. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  5. 2017Graph Attention Networkscited by 8,923
  6. 2017Inductive Representation Learning on Large Graphscited by 6,320
  7. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  8. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,358
  9. 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 609
  10. 2016Reinforcement Learning with Unsupervised Auxiliary Taskscited by 404
  11. 2020Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cellscited by 90
  12. 2014Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasetscited by 72
8 more
  1. 2020SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Liftingcited by 23
  2. 2018Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networkscited by 19
  3. 2021SPATE-GAN: Improved Generative Modeling of Dynamic Spatio-Temporal Patterns with an Autoregressive Embedding Losscited by 13
  4. 2020Residual Correlation in Graph Neural Network Regressioncited by 11
  5. 2019Structured Variational Inference in Continuous Cox Process Modelscited by 8
  6. 2020Auxiliary-task learning for geographic data with autoregressive embeddingscited by 8
  7. 2019MERL: Multi-Head Reinforcement Learningcited by 5
  8. 2019Augmenting correlation structures in spatial data using deep generative modelscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 4; 2024: 7; 2025: 4; 2026: 3.

20 papers.

Sort:

  1. 2023Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networkscited by 61
  2. 2025Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networkscited by 14
  3. 2023Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networkscited by 13
  4. 2023GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinatescited by 12
  5. 2024Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical Learningcited by 5
  6. 2022Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Datacited by 4
  7. 2024Spatioformer: A Geo-encoded Transformer for Large-Scale Plant Species Richness Predictioncited by 4
  8. 2022Graph Neural Networks with Precomputed Node Featurescited by 3
  9. 2023Inductive biases in deep learning models for weather predictioncited by 3
  10. 2024Beyond Grid Data: Exploring Graph Neural Networks for Earth Observationcited by 3
  11. 2026Mobility-Embedded POIs: Learning What A Place Is and How It Is Used from Human Movementcited by 3
  12. 2024Positional Encoder Graph Quantile Neural Networks for Geographic Datacited by 1
8 more
  1. 2024Disentangled and Self-Explainable Node Representation Learningcited by 1
  2. 2025Urban Incident Prediction with Graph Neural Networks: Integrating Government Ratings and Crowdsourced Reportscited by 1
  3. 2024Spatial Transfer Learning with Simple MLPcited by 0
  4. 2024Introduction to Graph Neural Networks for Machine Learning Engineerscited by 0
  5. 2025On the use of Graphs for Satellite Image Time Seriescited by 0
  6. 2025RelMap: Reliable Spatiotemporal Sensor Data Visualization via Imputative Spatial Interpolationcited by 0
  7. 2026Multi-Modal Spatio-Temporal Graph Neural Network with Mixture of Experts for Soil Organic Carbon Predictioncited by 0
  8. 2026SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysiscited by 0