We have published two new papers in December:
- The Node Vector Distance Problem in Complex Networks, by M. Coscia, A. Gomez-Lievano, F. Neffke, published in ACM Computing Surveys

The paper develops a new measure to quantify the distances between sets of nodes, with important applications on network dynamics such as spread of diseases.
Read Michele’s blog post about it: https://www.michelecoscia.com/?p=1898 - Data-driven strategies for optimal bicycle network growth, by L.G. Natera Orozco, F. Battiston, G. Iniguez, M. Szell, published in Royal Society Open Science

Here we investigate the network structure of bicycle networks in cities around the world, and find that they consist of hundreds of disconnected patches, even in cycling-friendly cities like Copenhagen. To connect these patches, we develop and apply data-driven, algorithmic network growth strategies, showing that small but focused investments allow to significantly increase the connectedness and directness of urban bicycle networks.






We think it is important to lift people out of poverty and to guarantee them decent standards of living. However, to successfully promote economic growth, the high degree of complexity of the global market and regional industrial activities requires an integrated understanding of the ecosystem of complementary actors, knowhow, and capital. The way to do so is by conceptualizing productivity as an emerging property of a complex system made by simpler interacting parts. Complex systems are notoriously difficult to control but quantifying these interactions can identify the bottlenecks to growth and inform policy to bolster economic convergence. Using tools from economics, complex systems, and network science, we seek crucial insights that enable economic convergence. 



Vedran is a well-rounded scientist with a professional background from tech, academia, and the international development sector, starting at ITU as Assistant Professor. His work lies in the intersection between network science, ethics and computer science, harnessing the power of complex networks, massive datasets, machine learning and data visualization for public good. Vedran joined from UNICEF where he was a Principal Researcher focused on understanding how modern technologies, such as Machine Learning and Artificial Intelligence, impact our societies and its most vulnerable communities. His previous work has been covered in The Ecomomist, Forbes, Scientific American, and Die Zeit, and been featured on the cover of the Proceedings of the Natural Academy of Sciences.

