https://github.com/saezlab/network_tools
Collection of Python functions to run network-based analysis in signed and directed networks.
https://github.com/saezlab/network_tools
igraph networks networks-biology networkx python3 r
Last synced: about 2 months ago
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Collection of Python functions to run network-based analysis in signed and directed networks.
- Host: GitHub
- URL: https://github.com/saezlab/network_tools
- Owner: saezlab
- License: gpl-3.0
- Created: 2019-09-12T12:22:25.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2020-04-06T18:40:54.000Z (about 6 years ago)
- Last Synced: 2025-01-22T07:37:39.908Z (over 1 year ago)
- Topics: igraph, networks, networks-biology, networkx, python3, r
- Language: Jupyter Notebook
- Homepage: https://github.com/rosherbal/network_tools
- Size: 156 KB
- Stars: 1
- Watchers: 3
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.rst
- License: LICENSE.md
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README
network_tools
##########
Collection of Python and R functions to run network-based analysis
in signed and directed networks.
Follow the jupyter notebook tutorial to see what you can do.
About the code
================================
Developed on:
- python 3.7.3 installed through ANACONDA [Clang 4.0.1 (tags/RELEASE_401/final)] in macOS Catalina version 10.15.1.
**Packages (Version)**
* pandas (1.0.1)
* networkx (2.4.0)
* numpy (1.18.1)
- R 3.6.0 (2019-04-26) -- "Planting of a Tree" Platform: x86_64-apple-darwin15.6.0 (64-bit)
Notes
================================
1. Weight and sign are collapse in the same value: weight.
Negative weights indicate inhibition, while positive one are activations.
2. We use directed graphs to calculate network and node-based measurements.
This type of graphs only allow ONE interaction between two nodes.
As the weight contains also de sign,
if an interaction is repeated with opposite signs between 2 nodes
(e.g. A -| B; A -> B), only one weight can be kept.
Thus, the weight that is kept is the highest (keeping the sign).
Measurements
================================
The function __get_measurments__ calculate the follow network's features:
* Number of edges
* Number of nodes
* Density
* Avg betweenness centrality
* Avg degree centrality
* Avg eigenvector centrality