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Python package to build and manipulate temporal graphs using NetworkX as backend.
Package is available to install on PyPI:
$ pip install 'networkx-temporal[draw]'
The
[draw]
extra includes additional libraries required to plot graphs with NetworkX.
For detailed information on using the package, please refer to its official documentation.
An interactive Jupyter notebook with more examples is also available here (open on Colab).
>>> import networkx_temporal as tx
>>>
>>> # Build directed temporal graph.
>>> TG = tx.TemporalDiGraph()
>>> TG.add_edge("a", "b", time=0)
>>> TG.add_edge("c", "b", time=1)
>>> TG.add_edge("d", "c", time=2)
>>> TG.add_edge("d", "e", time=2)
>>> TG.add_edge("a", "c", time=2)
>>> TG.add_edge("f", "e", time=3)
>>> TG.add_edge("f", "a", time=3)
>>> TG.add_edge("f", "b", time=3)
>>>
>>> # Slice it into snapshots.
>>> TG = TG.slice(attr="time")
>>>
>>> # Plot resulting object.
>>> tx.draw(TG, layout="kamada_kawai", figsize=(8,2))
Contributions are welcome! If you find any bugs or have any suggestions, feel free to open a ticket, fork the repository and create a pull request, or simply send an e-mail. Please keep in mind that any out-of-scope contributions (not regarding temporal networks) should instead be directed to the NetworkX repository.
This package is released under the BSD License.
In case this package is useful for your research, please kindly consider citing it.
FAQs
Python package to build and manipulate dynamic NetworkX graphs.
We found that networkx-temporal demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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Socket MCP brings real-time security checks to AI-generated code, helping developers catch risky dependencies before they enter the codebase.
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