Fariba KARIMI - Graz University of Technology, Austria
December 1st, 2026
Huijuan WANG - TU Delft, Netherlands
December 1st, 2026
Biography
Dr. Huijuan Wang is an Associate Professor in the department of Intelligent Systems at Delft University of Technology. Her research focuses on network data science. She develops methodologies to model, control and predict dynamic processes on time-evolving complex networks. Her work addresses diverse applications, ranging from epidemic spreading and opinion interactions to social and financial contagion, resilience of infrastructures and the organisation of criminal networks. Dr. Wang was a visiting scientist in the Department of Physics at Boston University (2011-2019), as well as in the Departments of Electrical Engineering at Stanford (2015) and Princeton (2022) Universities. She is the Co-founder of the Dutch Network Science Society. She has served as Chair of the Netherlands Platform for Complex Systems and as a board member of the Network Science Society.
Abstract
Human social interactions or contacts usually occur at specific times rather than continuously. They can be represented as temporal networks or as temporal hypergraphs when an interaction involves a group of individuals. Such networks facilitate the spread of information, opinions and epidemics. In this talk, we study these spreading processes on temporal networks and temporal hypergraphs. We address problems regarding influence estimation, spreading trajectories, and network robustness with the objective of identifying the relation between a node’s or link’s properties in the network and its role in the dynamic process. Specifically, how can we utilize the partially observed temporal network around each node to estimate the ranking of nodes by spreading influence? The average number of nodes infected over a given period starting from a single seed node is called the influence of that node. Which types of hyperlinks contribute more to a spreading process, i.e., lead to more nodes being infected directly via their activation? Which types of links, when removed, most strongly degrade the efficiency of information transport? To this end, we introduce theoretically grounded centrality metrics specially designed for addressing these problems.
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.