Metadata-Version: 2.1
Name: TemporalBackbone
Version: 0.1.0
Summary: A tool to detect the backbone in temporal networks
Home-page: https://github.com/matnado/TemporalBackbone
Author: Matthieu Nadini
Author-email: matthieu.nadini@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: POSIX :: Linux
Requires-Python: >=3.6
Description-Content-Type: text/markdown

# A tool to detect the backbone in temporal networks

An efficient and fast tool to detect the backbone network in temporal networks. For accurate results, it should be applied to networks with at least 1,000 nodes.

The computational time is O(N_E T^2), where N_E are the number unique edges in the network and T the number of time steps. 

For sparse networks (like most of the large networks), the computational time is O(N T^2)


How to install it 

```
pip install TemporalBackbone
```

How to run the package

```
import TemporalBackbone as TB

data = TB.Read_sample()
TB.Temporal_Backbone(data)
```


Input: 
- pandas dataframe with three columns: ***node1, node2, time*** *(order is important)*
- Minimum length of the interval: ***default 1 day** (time step is taken from the data)*
- whether the network is directed or not: ***default True***
- whether to use the Bonferroni correction: ***default True***
- threshold to determine the significance of a link: ***default 0.01***

Output:
- list with the significant links    




### Please cite

The methodology is first introduced in 
*Nadini, M., Bongiorno, C., Rizzo, A., & Porfiri, M. (2020). **Detecting network backbones against time variations in node properties.** Nonlinear Dynamics, 99(1), 855-878.*

Then was deemed as appropriate for large temporal networks, having a good trade-off between false positives and false negatives. See
*Nadini, M., Rizzo, A., & Porfiri, M. (2020). **Reconstructing irreducible links in temporal networks: which tool to choose depends on the network size.** Journal of Physics: Complexity, 1(1), 015001.*



