In the past two years, NERDS member Michele Coscia has been working on a textbook for the Network Analysis class he teaches at ITU. This “Atlas for the Aspiring Network Scientist”, has now reached version 1.0, and 760 pages, and is available for anyone to read for free: https://arxiv.org/abs/2101.00863
Website: https://www.networkatlas.eu/
The Atlas aims at being broad, not deep, to be a pointer to the things you need to know about Network Science rather than a deep explanation of those things.
Consider this a v1.0 of a continuous effort. There are many things to improve: language, concepts, references, figures. Please contact Michele with any comments.
Michele also plans to have interactive figures on the website in the future. Version 1.0 was all financed using his research money and time. For the future, Michele will need some support to do this in his free time. If you feel like encouraging this effort, you can consider becoming a member on Patreon.
Find a more detailed explanation of The Atlas for the Aspiring Network Scientist on Michele’s page: https://www.michelecoscia.com/?p=1913










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.