tgm-team / tgm
Efficient and Modular ML on Temporal Graphs
Why this job: works in the areas we cover, but does not say which job it does
What it does
- Unified Temporal API. supports both continuous-time and discrete-time graphs, and graph discretization
- Efficiency. ~7.8× faster training and ~175× faster discretization vs. existing research libraries
- Research-Oriented. modular hook framework standardizes workflows for link, node, and graph-level tasks
- Datasets. built-in support for popular datasets (e.g., TGB[^12])
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Active
- Last commit 3 days ago
- Stars
- 126
- On GitHub
- Licence
- MIT
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- None yet
- No releases
- Built with
- Python
- 98% of the code
Is it looked after?
- Contributors
- 2
- Last 90 days
- Issues
- 91% closed
- 19 still open
- Pull requests
- 187 merged
- 3 waiting
- Releases
- None
- Never published
- Contributing guide (opens in new tab)
- No security policy
- No code of conduct
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More: languages, topics and project record
Languages
- Python 98%
- Shell 2%
Topics
- geometric-deep-learning
- temporal-graphs
- temporal-graph-networks
- temporal-graph-neural-networks
- machine-learning
Project record
- JobOther AI tools
- StatusListed
- Commits560
- Forks20
- Watching4
- Stars in 28 days+2
- CreatedJun 11, 2025
- First seen by usNov 7, 2026
- CheckedNov 12, 2026