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.
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)
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).
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