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.

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).

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.

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

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

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.

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)

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

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

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