Scientific networks are often used to quantify science, ranging from the impact of researchers, journals, or universities over to measuring the interdisciplinarity or disruptiveness of scientific work. In this talk I present relational hyperevent models (RHEM) as a general model for the joint dynamics of coauthoring and citation networks. RHEM can account for polyadic dependencies in the data, i.e., interactions among more than just two nodes, which cannot be well represented by a purely dyadic specification of the event rate. Moreover, I discuss an extension of the model to RHEM with random node-level effects representing the latent popularity of papers and researchers and briefly overview practical aspects, including computational issues, in specifying and estimating RHEM with the open-source software eventnet (https://github.com/juergenlerner/eventnet).

Bio: Jürgen Lerner has a PhD in Computer Science from the University of Konstanz, where he leads the DFG-funded project "Statistical analysis of time-stamped multi-actor events in social networks". His general research areas are social network analysis and computational social science and his work is characterized by tight collaboration with social scientists. His research interests include statistical network modeling, networks of relational events, networks with positive and negative ties, analysis of open-peer production, and personal network analysis. Jürgen Lerner developed or contributed to various network analysis software, including visone and eventnet. He regularly teaches courses which are attended by students of computer science and the social sciences alike. From April 2022 to September 2023 he was interim professor for Computational Social Sciences and Humanities at the RWTH Aachen.