Martina Contisciani

Martina Contisciani is a postdoctoral researcher in the Department of Network and Data Science at the Central European University and a visiting postdoc in the Inverse Complexity Lab at IT:U, led by Prof. Peixoto. She completed her PhD in Computer Science at the Max Planck Institute for Intelligent Systems and the University of Tübingen in 2024. Her research interest lies in the development of statistical methods and algorithms to study complex systems, with a focus on network inference. Currently, she is working on the MOMA (Multiscale network modelling of migration flows in Austria) project.

graph-tool

A comprehensive Python network library, including structural, dynamical and statistical algorithms, as well as visualization. Free and Open Source, released under the LGPLv3 license, fully documented, with high-performance algorithms written in C++, using template metaprogramming and OpenMP parallelism. Available at: https://graph- tool.skewed.de

Netzschleuder

Online catalogue and repository of over 160K network datasets with the aim of aiding scientific research. The website is meant to be browsed both by humans and machines alike, and can also be accessed via a convenient JSON API, or via the graph-tool library. The network datasets themselves are available in several machine-readable formats, in particular gt, GraphML, GML and CSV. Available at: https://networks.skewed.de

Complex Systems and Network Science