mlsanityData to 14 Oct 2026

STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions

arXiv 2509.10528 ↗First posted 4 Sep 2025cs.LG cs.AIcited by 2

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Abstract (arXiv)

Urban spatio-temporal data present unique challenges for predictive analytics due to their dynamic and complex nature. We introduce STM-Graph, an open-source Python framework that transforms raw spatio-temporal urban event data into graph representations suitable for Graph Neural Network (GNN) training and prediction. STM-Graph integrates diverse spatial mapping methods, urban features from OpenStreetMap, multiple GNN models, comprehensive visualization tools, and a graphical user interface (GUI) suitable for professional and non-professional users. This modular and extensible framework facilitates rapid experimentation and benchmarking. It allows integration of new mapping methods and custom models, making it a valuable resource for researchers and practitioners in urban computing. The source code of the framework and GUI are available at: https://github.com/Ahghaffari/stm_graph and https://github.com/tuminguyen/stm_graph_gui.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 14 of its 32 references that are in the map. First posted on arXiv 4 Sep 2025.

Builds on

14 of 32 references.

References · 14

  1. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  2. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 401
  3. 2021Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solutioncited by 157
  4. 2018Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Predictioncited by 122
  5. 2021PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Modelscited by 118
  6. 2021DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Predictioncited by 73
  7. 2025Modeling and Analyzing Urban Networks and Amenities with OSMnxcited by 25
  8. 2023UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Predictioncited by 20
  9. 2024FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Predictioncited by 18
  10. 2023Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecastingcited by 16
  11. 2023Explainable Spatio-Temporal Graph Neural Networkscited by 13
  12. 2023Spatio-Temporal Meta Contrastive Learningcited by 11
2 more
  1. 2024EasyST: A Simple Framework for Spatio-Temporal Predictioncited by 6
  2. 2024Using Graph Neural Networks to Predict Local Culturecited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Knowledge-Integrated Representation Learning for Crypto Anomaly Detection under Extreme Label Scarcity; Relational Domain-Logic Integration with Retrieval-Grounded Context and Path-Level Explanationscited by 0
  2. 2026Graph-Conditioned Mixture of Graph Neural Network Experts for Traffic Forecastingcited by 0