netsci

Analyzing Complex Networks with Python

Author	Version	Demo
Gialdetti

netsci is a python package for efficient statistical analysis of spatially-embedded networks. In addition, it offers efficient implementations of motif counting algorithms. For other models and metrics, we highly recommend using existing and richer tools. Noteworthy packages are the magnificent NetworkX, graph-tool or Brain Connectivity Toolbox.

Installing

Install and update using pip:

$ pip install netsci

A simple example

Analyzing a star network (of four nodes)

>>> import numpy as np
>>> import netsci.visualization as nsv
>>> A = np.array([[0,1,1,1], [0,0,0,0], [0,0,0,0], [0,0,0,0]])
>>> nsv.plot_directed_network(A, pos=[[0,0],[-1,1],[1,1],[0,-np.sqrt(2)]])

>>> import netsci.metrics.motifs as nsm
>>> f = nsm.motifs(A, algorithm='brute-force')
>>> print(f)
[1 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0]

>>> nsv.bar_motifs(f)

Testing

After installation, you can launch the test suite:

$ pytest

Help and Support

Examples

Theme	MyBinder	Colab
Basic network motifs demo
Connectomics dataset, and 3-neuron motif embedding

Communication

Please send any questions you might have about the code and/or the algorithm to eyal.gal@mail.huji.ac.il.

Citation

If you use netsci in a scientific publication, please consider citing the following paper:

Gal, E., Perin, R., Markram, H., London, M., and Segev, I. (2019). Neuron Geometry Underlies a Universal Local Architecture in Neuronal Networks. BioRxiv 656058.

Bibtex entry:

@article {Gal2019
    author = {Gal, Eyal and Perin, Rodrigo and Markram, Henry and London, Michael and Segev, Idan},
    title = {Neuron Geometry Underlies a Universal Local Architecture in Neuronal Networks},
    year = {2019},
    doi = {10.1101/656058},
    journal = {bioRxiv}
}