tgm-team/tgm
Efficient and Modular ML on Temporal Graphs
This tool helps researchers and practitioners build and experiment with machine learning models on dynamic, evolving graphs, like social networks, transaction logs, or interaction data. It takes your temporal graph data as input and efficiently processes it to output predictions for future links, node properties, or overall graph behavior. It's designed for machine learning engineers and researchers working with complex, time-series graph data.
Use this if you need to quickly prototype and train machine learning models on large, time-evolving graph datasets with high efficiency.
Not ideal if you are a business user looking for an off-the-shelf solution for static graph analysis or basic graph visualization.
Stars
96
Forks
16
Language
Python
License
MIT
Category
Last pushed
Mar 12, 2026
Commits (30d)
0
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