Tiago Peixoto @tiago@social.skewed.de

We will be offering a limited number of 🚨full scholarships🚨, covering full tuition waiver and housing aid.

The program is hosted by @dnds_ceu@twitter.com at @ceu@twitter.com, in Vienna 🇦🇹, often ranked the most livable city in the world.

Deadlines: Feb. 1, Apr. 12, 2021

Spread the word!

3/3

Exciting news! We are announcing a new undergraduate program in Quantitative Social Sciences at @dnds_ceu@twitter.com.

https://undergraduate.ceu.edu/qss/about

Our program combines rigorous ("hard sciences"-level) mathematics, statistics, and programming with the pillars of the social sciences. 1/3

New version of graph-tool is out!
https://graph-tool.skewed.de

(New: https://graph-tool.skewed.de/static/doc/demos/inference/inference.html#latent-triadic-closures)

Single-line install instructions:

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

Homebrew ⤵️
brew install graph-tool

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

New on the @arxiv_physics: "Disentangling homophily, community structure and triadic closure in networks", https://arxiv.org/abs/2101.02510

Homophily/communities and triadic closure (triangles) are conflated properties in network analysis, and this method tells them apart.

New work on the arxiv: "Network reconstruction and community detection from dynamics",
https://arxiv.org/abs/1903.10833

I show how coupling Bayesian network reconstruction from functional behavior with community detection enhances both tasks simultaneously.

Just out on PRX: "Reconstructing Networks with Unknown and Heterogeneous Errors"

New method can reconstruct networks and provide error estimates for them, even when measurement uncertainties are unknown.

https://journals.aps.org/prx/abstract/10.1103/PhysRevX.8.041011

Code is available as part of graph-tool: https://graph-tool.skewed.de

The documentation for the reconstruction code is here: https://graph-tool.skewed.de/static/doc/demos/inference/inference.html#network-reconstruction

Each integer is represented in a high-dimensional space, and gets squished down to 2D so that numbers with similar prime factorisations are closer together than those with dissimilar factorisations.

https://johnhw.github.io/umap_primes/index.md.html

Via @svscarpino@twitter.com.

Finally on the arXiv: "Reconstructing networks with unknown and heterogeneous errors" https://arxiv.org/abs/1806.07956

Did you know you can reconstruct and make error estimates for networks, by making only a single noisy measurement?