tiago,
@tiago@social.skewed.de avatar

Good news everyone! A new version of :gt: graph-tool is just out! @graph_tool

https://graph-tool.skewed.de

:gt: @graph_tool is a comprehensive and efficient :python: Python library to work with networks, including structural, dynamical and statistical algorithms, as well as visualization.

It uses :cpp: C++ under the hood for the heavy lifting, making it quite fast.

This version includes new features, bug fixes, and improved documentation: https://graph-tool.skewed.de/static/doc/index.html

One of the new features is scalable and principled network reconstruction: https://graph-tool.skewed.de/static/doc/demos/reconstruction_indirect/reconstruction.html

Single line installation:

Anaconda ⤵️
conda create --name gt -c conda-forge graph-tool

Homebrew ⤵️
brew install graph-tool

Debian/Ubuntu ⤵️
apt-get install python3-graph-tool

Gentoo ⤵️
emerge graph-tool

Docker ⤵️
docker pull tiagopeixoto/graph-tool

You can also play it with in colab: https://colab.research.google.com/github/count0/colab-gt/blob/master/colab-gt.ipynb

@networkscience
@datascience
@python

image/png
image/png

neuralreckoning,
@neuralreckoning@neuromatch.social avatar

@tiago @graph_tool @networkscience @datascience @python looks cool! Have you seen our stochastic gradient descent layout algorithm? Fast and high quality with c++ code you can help yourself to.

https://github.com/jxz12/s_gd2

tiago,
@tiago@social.skewed.de avatar
halama_immuno,
@halama_immuno@mstdn.science avatar
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