Metadata-Version: 2.1
Name: TemporalBackbone
Version: 0.1.6
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
Description: # 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 I_{max}^2), where N_E are the number of unique edges in the network and I_{max} the maximum number of intervals. I_{max} can be computed as T (total time steps) divided by the minimum length of the interval, \Delta I_{min}. 
        
        For sparse networks (like most of the large networks), the computational time is O(N I_{max}^2)
        
        
        How to install it 
        
        ```
        pip install TemporalBackbone
        ```
        
        In order to run the library, additional packages should be installed
        
        ```
        pip install wget 
        wget https://raw.githubusercontent.com/matnado/TemporalBackbone/main/TemporalBackbone/requirements.txt
        pip install -r requirements.txt
        ```
        
        This implementation assumes that packages like `copy`, `collections`, and `time` are already present, because they cannot be installed via `pip install`. 
        
        
        How to run the package
        
        ```
        import TemporalBackbone as TB
        
        data = TB.Read_sample()
        TB.Temporal_Backbone(data)
        ```
        
        Input: 
        - **df** pandas dataframe with three columns: ***node1, node2, time*** *(order is important)*
        - **I_min** minimum length of the interval, written in seconds: ***default 1 day or 60x60x24 seconds** (time step is taken from the data)*
        - **is_directed** whether the network is directed or not: ***default True***
        - **Bonferroni** whether to use the Bonferroni correction: ***default True***
        - **alpha** 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.*
            
        
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
