Fariba KARIMI - Graz University of Technology, Austria
December 1st, 2026
Huijuan WANG - TU Delft, Netherlands
December 1st, 2026
This tutorial offers a guided tour of the open, evolving textbook Network Science Data & Models, a Jupyter Book that accompanies the Python-based network analysis course at Northeastern University. We begin with a concise walkthrough of the early chapters—data ingestion, descriptive measures, community detection, generative models, and network dynamics—highlighting the notebooks already included in the textbook. The second half is a focused, hands-on exploration of spatial networks. Starting with random geometric graphs as baselines, we transform real-world shapefiles into planar graphs using GeoPandas, Shapely, and OSMnx. We then enrich these graphs with population-level data and show several statistical approaches for analyzing spatial data. Participants will leave with reproducible notebooks, curated datasets, and clear guidance on submitting pull requests to contribute their own chapters to this book in the future. The goal is not only to discuss spatial network analysis but also to empower the community to extend this shared, open textbook in support of both research and teaching.
Biography
Brennan Klein is core faculty at the Network Science Institute at Northeastern University and Assistant Teaching Professor in the Department of Physics. He is the director of the Complexity & Society Lab, which spans two broad research areas: 1) Information, emergence, and inference in complex systems — developing tools and theory for characterizing dynamics, structure, and scale in networks, and 2) Public health and public safety — creating and analyzing large-scale datasets that reveal inequalities in the U.S., from epidemics to mass incarceration. In 2023, Prof. Klein was awarded the René Thom Young Researcher Award, given to a researcher to recognize substantial early career contributions and leadership in research in Complex Systems-related fields. He received a PhD in Network Science in 2020 from Northeastern University and a BA in Cognitive Science from Swarthmore College in 2014.
Despite its inevitable nature and the incontrovertible wisdom that “failure is the mother of success”, our quantitative understanding of failure (and how it leads to success) remains limited, in part due to the lack of systematic data and methods that record the frequently occurring yet often neglected failures within individuals, teams and organizations. This situation is changing radically, however, thanks to newly available large-scale datasets spanning social, scientific, and technical domains. In this tutorial, I will explore diverse examples of failure through the lens of data analytics, mathematical modeling, and causal inference. Drawing on computational tools from complexity and data sciences, I will show how a data-driven approach can illuminate the mechanisms behind the why, how, and when of failure. Beyond deepening our theoretical understanding of complex social systems, advances in this area have important policy implications and the potential to improve predictions, interventions, and designs that turn failure into a stepping stone for success.
Biography
Yian Yin is an assistant professor of information science at Cornell University. His research applies and develops novel computational tools to understand how individual, social, and environmental processes independently and jointly promote (or inhibit) scientific progress and innovation achievements. As a computational social scientist, he has also used science and innovation as a powerful lens to examine broader processes and outcomes in a wide range of complex social processes, from artistic and cultural productions to public policy, from media attention to market competition to human conflict. His research has been published in top general audience venues such as Science, Nature, and Nature Human Behaviour and featured in media outlets such as Forbes, Scientific American, The Atlantic, Harvard Business Review, and MIT Technology Review. Yian received his Ph.D. in Industrial Engineering and Management Science at Northwestern University, with research affiliations at Northwestern Institute on Complex Systems and Kellogg Center for Science of Science and Innovation. He holds bachelor's degrees in Statistics and Economics from Peking University.