infonet is a Python software package developed to:

1. Validate network inference algorithms based on multivariate trasfer entropy. This package was used to generate the results and plots published in [1].
2. Investigate the relationship between network structure and information dynamics, in particular transfer entropy. This package was used to generate the results and plots published in [2], linking pairwise transfer entropy to network motifs.
3. Compare the performance of bivariate and multivariate network inference algorithms. This package was used to generate the results and plots in [3], where we studied how well different algorithms can capture fundamental network properties (as compared to ground-truth structural networks with different topologies).

To get started, have a look at the wiki.

[1] Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing. Leonardo Novelli, Patricia Wollstadt, Pedro Mediano, Michael Wibral, and Joseph T. Lizier. Network Neuroscience (2019), 3:3, 827-847. doi: 10.1162/netn_a_00092

[2] Deriving pairwise transfer entropy from network structure and motifs. Leonardo Novelli, Fatihcan M. Atay, Jürgen Jost, and Joseph T. Lizier. Proceedings of The Royal Society A (2020), 476:20190779. doi: 10.1098/rspa.2019.0779 and arXiv:1911.02931

[3] Inferring network properties from time series via transfer entropy and mutual information: validation of bivariate versus multivariate approaches. Leonardo Novelli and Joseph T. Lizier. arXiv:2007.07500