lehaifeng/T-GCN

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1787 stars · 472 forks observed · 2026-10-20

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of research source code implementing Temporal Graph Convolutional Networks (T-GCN) and related variants for urban traffic flow prediction. It accompanies multiple published papers on spatiotemporal graph neural networks and graph representation learning.

Use cases

• predict urban traffic flow with graph neural networks
• implement temporal graph convolutional networks
• reproduce T-GCN paper results
• spatiotemporal traffic forecasting research
• learn graph representation learning for time series
• compare GNN traffic prediction models

When to choose

• you need reference implementations of T-GCN and its variants for traffic prediction
• you are doing academic research on spatiotemporal GNNs
• you want paper-reproducible code for graph-based forecasting

When to avoid

• you need a production-ready, maintained forecasting library
• you require a licensed package for commercial use (no license is provided)
• you need non-traffic graph learning tasks with polished APIs

Facets

library · maturity active

machine-learning deep-learning data-science machine-learning deep-learning artificial-intelligence python graph-neural-networks traffic-forecasting spatiotemporal time-series research-code jupyter-notebook

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