Fariba KARIMI - Graz University of Technology, Austria
December 1st, 2026
Huijuan WANG - TU Delft, Netherlands
December 1st, 2026
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.
Abstract
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
Fariba Karimi is a network scientist who develops mathematical and computational models to study inequalities in socio-technical networks and algorithms. She is currently a full professor of Data Science at the Faculty of Computer Science and Biomedical Engineering at the Graz University of Technology. In 2024, she received an ERC Starting Grant on the topic of Network Fairness. Fariba Karimi received her doctorate from the University of Umea in 2015. She then spent four years researching at the Computational Social Science Department at the Leibniz Institute for the Social Sciences in Cologne, Germany. Since March 2021, she has been the group lead of the “Network Inequality” group at the Complexity Science Hub Institute in Vienna. Before joining TU Graz, she also served as a tenure-track professor at the Department of Computer Science at Vienna University of Technology. In 2023, she received the prestigious Young Scientist Award from the German Physical Society for her contribution to modeling minorities and inequalities in networks.
Abstract
Are social networks fair playgrounds? What are the roles of structural features like homophily, group size imbalance, and degree of connectivity on access to information, visibility, and ranking systems? This tutorial addresses these questions and more. Drawing on network science, computational social science, and fairness research, we cover core concepts such as network mechanisms shaping inequalities, metrics for measuring representation and visibility of minorities, and methods for auditing bias in ranking and recommender algorithms built on network data. We discuss generative network models that reproduce real-world inequalities and learn practical approaches for evaluating fairness in empirical and synthetic networks.