Tiago Peixoto @tiago@social.skewed.de

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.

"Science starts with a question. Data science starts with the data. What makes data science so hard is that it starts in the wrong place."

Great analogy of scientific protocol with law & order episodes!

Now I can't get the title song out of my head.
https://simplystatistics.org/2018/08/15/the-law-and-order-of-data-science/

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?

Hello federated world!