GAlib

pyGAlib is a package for the analysis of graphs and complex networks in Python. It treats graphs as adjacency matrices in order to take advantage of the faster array manipulations with NumPy. The library is robust and transparent – it was designed with the goal in mind that one should always find the code that is doing the job, rather than having to navigate through a mess of files, classes and dependencies. Therefore, the library is easy to install, modify and extend.

$ pip install galib

GitHub: www.github.com/gorkazl/pyGAlib/
PyPI: www.pypi.org/project/galib/

PathLims

PathLims generates ultra-short and ultra-long networks, that is, (di)graphs with shortest or longest possible pathlength. Also, given any empirical network of arbitrary size and density, it also calculates the upper and the lower limits for their average pathlength and global efficiency.

PathLims is fully compatible with pyGAlib but it is released independently. Therefore, PathLims can be used along any other network analysis toolbox in Python, with the only requirement that (di)graphs are represented as adjacency matrices, given as NumPy arrays.

$ pip install pathlims

GitHub: www.github.com/gorkazl/pathlims/
PyPI: www.pypi.org/project/pathlims/

Related publications:

• Sizing complex networks.
  G. Zamora-López & R. Brasselet. Nature Comms. Phys. 2:144 (2019).

NetDynFlow

In the recent years, alternative methods to graph theory have been proposed in order to study networks. These methods involve the observation of diverse dynamical processes propagating along the network. NetDynFlow comprises the tools to describe and analyse networks based on the propagation of noisy signals (Ornstein-Uhlenbeck process). Under this formalization network metrics are represented as spatio-temporal properties of the network dynamics rather than combinatorial features of a graph.

GitHub: www.github.com/mb-BCA/NetDynFlow/

Related publications:

• Framework based on communicability and flow to analyze complex network dynamics.
  M. Gilson, N.E. Kouvaris, G. Deco & G. Zamora-López. Phys. Rev. E 97, 052301 (2018).
• Network analysis of whol-brain fMRI dynamics: A new framework based on dynamic communicability.
  M. Gilson, N.E. Kouvaris, G. Deco, et al. NeuroImage 201, 116007 (2019).
• Posterior integration and thalmo-frontotemporal broadcasting are impaired in dissorders of consciousness.
  R. Panda, A. López-González, M. Gilson et al. Pre-print (2021).

pyMOU

pyMOU is a package for the estimation of whole-brain effective connectivity (EC) from fMRI signals. It considers the multivariate Ornstein-Uhlenbeck process as the generative model for the networked brain activity. Application of NetDynFlow for functional imaging is often based on the study of the EC networks instead of on the study of the anatomical connections.

GitHub: www.github.org/mb-BCA/pyMOU/

Related publications:

• Estimation of Directed Effective Connectivity from fMRI Functional Connectivity Hints at Asymmetries of Cortical Connectome.
  M. Gilson, R. Moreno-Bote, A- Ponce-Alvarez, P. Ritter & G. Deco. PLoS Comput. Biol. 12(3):e1004762 (2016).