Structural Analysis Nestedness, Modularity and In-block

To perform structural analysis in binary unipartite and bipartite networks by means of nestedness, as defined by Solé-Ribalta et al, PRE 2018 (https://doi.org/10.1103/PhysRevE.97.062302), in-block nested and modularity.

Inputs:

1. path-folder = directory where the network data files are. The data files with .csv file extension. It can be either in edge list or adjacency matrix format with no headers.
2. bipartite = boolean to indicate if "filename" is a bipartite (True) or unipartite (False) network.
3. edge_data = boolean indicating the format of the data file. Three-column/edge list (True) or matrix format (False).

output:

1. The function return two .csv files for each generated network containing the label partitions from the Modularity and in-block nestednesss analysis.
2. An additional .csv file containing a table with name, value, value, and of value the networks

If for example we pass bipartite=True and edge_data=True, all the networks we want to analyze have to fulfill such conditions.

example:

python structural_analysis.py home/User/data/ True False

Nestedness and In-block nestedness

Both metrics are computed considering the condition to compare the paired overlap between pairs of nodes, in contrast with the NODF metrics that considers

Modularity and in-block nestedness optimization

The optimization of modularity and in-block nestedness was perform by employing the Extremal optimization algorithm (https://doi.org/10.1103/PhysRevE.72.027104). The main code for this function was written in c++ and should be compiled as a file with a .so extension for Python 3.x. This file will be imported to python as a library.

This will be possible for MacOS or Linux.

System Requirements

Compilers

1. Clang/LLVM 3.3 or newer (for Apple Xcode's clang, this is 5.0.0 or newer) or
2. GCC 4.8 or newer

Python 3.x.

pybind11 (via pip or conda)

To compile the c++ files

Compilation on Linux:

g++ -O3 -Wall -shared -std=c++11 -fPIC `python -m pybind11 --includes` EO_functions_bipartite.cpp -o extremal_bi.so

Compilation on MacOS:

g++ -O3 -Wall -shared -std=c++11 -undefined dynamic_lookup `python -m pybind11 --includes` EO_functions_bipartite.cpp -o extremal_bi.so

Citations

MJ Palazzi, J Borge-Holthoefer, CJ Tessone and A Solé-Ribalta. Macro- and mesoscale pattern interdependencies in complex networks. J. R. Soc. Interface, 16, 159, 20190553 (2019). DOI: 10.1098/rsif.2019.0553

MJ Palazzi, A Solé-Ribalta, V Calleja-Solanas, CA Plata, S Meloni, S Suweis and J Borge-Holthoefer. An ecological approach to structural flexibility in online communication systems. Nature Communications, 12, 1941 (2021). DOI: 10.1038/s41467-021-22184-2