temporal-recovery-tree-py

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temporal-recovery-tree-py

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Recover temporal information from grown trees, using Python

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# Inference and temporal recovery in grown trees

Python implementation of efficient recovery algorithms for grown trees, introduced in "*[Inference, Model Selection, and the Combinatorics of Growing Trees](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.126.038301)*".

![](https://raw.githubusercontent.com/gcant/temporal-recovery-tree-py/master/splash.png)

## Dependencies

* [`networkx`](https://networkx.github.io)
* `numpy`
* [`mpmath`](http://mpmath.org/) (optional --- needed for one-node marginals)

## Code overview

* `temporal_recovery.py` contains our algorithms for recovering the history of a tree.
* `boundary_sampler.py` is a Python implementation of the data structure described in section 3.2.2 of ["*Efficient sampling of spreading processes on complex networks using a composition and rejection algorithm*"](https://arxiv.org/abs/1808.05859)
* `full_marginal.py` contains code to compute the exact one-node marginals.
* A C++/Python version of this code is available at [https://github.com/gstonge/fasttr](https://github.com/gstonge/fasttr)

## Reference

If you use this code, please consider citing:

"[*Inference, Model Selection, and the Combinatorics of Growing Trees*](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.126.038301)"<br/>
[G. T. Cantwell](https://www.george-cantwell.com), [G. St-Onge](https://gstonge.github.io) and [J.-G. Young](http://jgyoung.ca)<br/>
Physical Review Letters **126**, 038301 (2021)<br/>
[arXiv link](https://arxiv.org/abs/1910.04788) | 
[Journal link](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.126.038301) <br/>

## Author information

Code by [George Cantwell](https://www.george-cantwell.com). Don't hesitate to get in touch at <gcant@umich.edu>, or via the [issues](https://github.com/gcant/temporal-recovery-tree-py/issues)!

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