Abstract: Graph neural networks (GNNs) have demonstrated significant success in solving real-world problems using both static and dynamic graph data. While static graphs remain constant, dynamic ...

This repository contains the official PyTorch implementation and the UMC4/12 Dataset for the paper: [UrbanGraph: Physics-Informed Spatio-Temporal Dynamic Heterogeneous Graphs for Urban Microclimate ...

Dynamic Graph Neural Networks (Dynamic GNNs) have emerged as powerful tools for modeling real-world networks with evolving topologies and node attributes over time. A survey by Professors Zhewei Wei, ...

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...

What if you could transform vast amounts of unstructured text into a living, breathing map of knowledge—one that not only organizes information but reveals hidden connections you never knew existed?

Abstract: The graph coloring problem is a well-known optimization challenge, particularly relevant in dynamic environments where the graph undergoes continuous changes over time. Evolutionary ...

Grok the faster interpreter in Python 3.14, learn what’s new in Python packages and PyPI, explore the new Python-to-C features in Cython 3.1, and seize the power of Python’s abstract base classes. In ...

Hazmat suits, gloves, a respirator — Fabiola Menchelli needs all of it to handmake her large format art. For her new certain silence project, the artist from Mexico City worked with toxic color ...

Python's "abstract base class" system gives you a way to create types that serve as the abstract foundation for another, more concrete type. This example shows how an abstract base class from the ...