Dr. Cornelius Fritz
Assistant Professor, School Office - Computer Science & Stats
Publications and Further Research Outputs
Peer-Reviewed Publications
Cornelius Fritz, Michael Schweinberger, S. Bhadra, David R. Hunter, A Regression Framework for Studying Relationships among Attributes under Network Interference, Journal of the American Statistical Association, 2026, p1 - 22, p1-22
Fritz Cornelius, Mehrl Marius, Thurner Paul W., Kauermann Göran, Exponential random graph models for dynamic signed networks: An application to international relations, Political Analysis, in print, 2025
Alejandro Espinosa"Rada, Jürgen Lerner, Cornelius Fritz, Socio-cognitive networks between researchers: Investigating scientific dualities with the group-oriented relational hyperevent model, Social Networks, 83, 2025, p1 - 13, p1-13
Espinosa-Rada Alejandro, Lerner Jürgen, Fritz Cornelius, Socio-cognitive networks between researchers, Social Networks, 83, 2025, p1 - 13
Håvard Hegre and ... and Cornelius Fritz ..., The 2023/24 VIEWS Prediction Challenge: Predicting the Number of Fatalities in Armed Conflict, with Uncertainty, Journal of Peace Research, 2025
Fritz Cornelius, Dworschak Christoph, Mehrl Marius, Predicting uncertainty in stages: Using a semiparametric hierarchical hurdle model for predicting distributions of conflict fatalities , 2024, -
Fritz Cornelius, Georg Co-Pierre, Mele Angelo, Schweinberger Michael, A strategic model of software dependency networks , ArXiv e-prints , 2024
Kook Lucas, Schiele Philipp, Kolb Chris, Dold Daniel, Arpogaus Marcel, Fritz Cornelius, Baumann Philipp, Kopper Philipp, Pielok Tobias, Dorigatti Emilio, Rügamer David, Can inverse conditional flows serve as a substitute for distributional regression model in statistics?, 2024
De Nicola Giacomo, Fritz Cornelius, Mehrl Marius, Kauermann Göran, Dependence matters: Statistical models to identify the drivers of tie formation in economic networks, Journal of Economic Behavior & Organization, 215, 2023, p351 - 363
Rügamer David, Kolb Chris, Fritz Cornelius, Pfisterer Florian, Bischl Bernd, Shen Ruolin, Bukas Christina, de Andrade e Sousa Lisa Barros, Thalmeier Dominik, Baumann Philipp, Klein Nadja, Müller Christian L., deepregression: a flexible neural network framework for semi-structured deep distributional regression, Journal of Statistical Software, 105, (2), 2023, p1 - 31
Fritz Cornelius, Mehrl Marius, Thurner Paul W., Kauermann Göran, All that glitters is not gold: Relational events models with spurious events, Network Science, 11, (Special Issue 2), 2023
Fritz Cornelius, De Nicola Giacomo, Kevorg Sevag, Harhoff Dietmar, Kauermann Göran, Modelling the large and dynamically growing bipartite network of German patents and inventors, Journal of the Royal Statistical Society. Series A (Statistics in Society), 186, (3), 2023, p557 - 576
Fritz Cornelius, De Nicola G., Günther F., Rügamer D., Rave M., Schneble M., Bender A., Weigert M., Brinks R., Hoyer A., Berger U., Küchenhoff H., Kauermann G., Challenges in interpreting epidemiological surveillance data - Experiences from Germany, Journal of Computational and Graphical Statistics, 3, 2023
Schweinberger Michael, Fritz Cornelius, Discussion of "A tale of two datasets: Representativeness and generalisability of inference for samples of networks" by Pavel N. Krivitsky, Pietro Coletti, and Niel Hens, Journal of the American Statistical Association, (OnlineFirst), 2023, p1 - 5
Cornelius Fritz, Marius Mehrl, Paul W. Thurner, Göran Kauermann, The role of governmental weapons procurements in forecasting monthly fatalities in intrastate conflicts: A semiparametric hierarchical hurdle model, International Interactions, 2022
Fritz Cornelius, Dorigatti Emilio, Rügamer David, Combining graph neural networks and spatio-temporal disease models to predict COVID-19 cases in Germany, Scientific Reports, 3930, (12), 2022, p1 - 18
Fritz Cornelius, Kauermann Göran, On the interplay of regional mobility, social connectedness, and the spread of COVID-19 in Germany, Journal of the Royal Statistical Society. Series A (Statistics in Society), 185, (1), 2022, p400 - 424
Fritz Cornelius, De Nicola Giacomo, Rave Martje, Weigert Maximilian, Berger Ursula, Küchenhoff Helmut, Kauermann Göran, Statistical modelling of COVID-19 data: Putting generalised additive models to work, Statistical Modelling, (OnlineFirst), 2022
Berger Ursula, Fritz Cornelius, Kauermann Göran, Reihentestungen an Schulen können die Dunkelziffer von COVID-19 Infektionen unter Schülern signifikant senken (english translation: Mandatory testing in schools can significantly reduce underreporting of COVID-19 infections among students with in-Class teaching compared to home schooling), Das Gesundheitswesen, 84, (6), 2022, p495 - 502
Cornelius Fritz, Paul W. Thurner, Göran Kauermann, Separable and semiparametric network-based counting processes applied to the international combat aircraft trades, Network Science, 2021, p1--21
Baumann Sandra A., Fritz Cornelius, Mueller Ralf S., Food antigen-specific IgE in dogs with suspected food hypersensitivity, Tierärztliche Praxis. Ausgabe K, Kleintiere/Heimtiere, 48, (6), 2020, p395 - 402
Fritz Cornelius, Lebacher Michael, Kauermann Göran, Tempus volat, hora fugit: A survey of tie-oriented dynamic network models in discrete and continuous time, Statistica Neerlandica, 74, (3), 2020, p275 - 299
Research Expertise
Projects
- Title
- Local Dependence in Large Event Data
- Summary
- In today"s interconnected world, digital platforms generate an unprecedented amount of network data, capturing social interactions such as messages and emails. These interactions form networks that evolve over time, providing valuable insights into communication patterns, behavior, and structural dependencies. However, traditional statistical models struggle to handle large-scale network data effectively due to assumptions about global dependencies and computational limitations. This project tackles these challenges by developing scalable statistical models for large-scale network data. The focus shifted from pure event data to general network data without temporal information due to a lack of basis of models for large static networks. This work serves as the basis for future extensions to the temporal domain. The research, thereby, introduces innovative methods based on local dependence, which assumes that units in a network are primarily aware of their local neighborhoods rather than the global network. Three main approaches guide the project: 1. Non-Overlapping Neighborhoods: Events are analyzed within distinct, isolated clusters of actors. 2. Domain-Driven Overlapping Neighborhoods: Actor interactions are driven by overlapping social contexts, such as shared affiliations or common partners. 3. Latent Social Spaces: Actor relationships are represented in a hidden space where proximity reflects the likelihood of interaction. These models are theoretically robust and computationally scalable, enabling efficient analysis of large networks. Practical applications range from information diffusion on social media to dependency networks between open-source software packages. At the same time, the project emphasizes reproducibility and accessibility, leading to the release of the software package bigergm for the analysis of big networks. This ensures that researchers and policymakers can apply these tools to real-world problems. This project bridges the gap between theoretical statistical modeling and practical large-scale event data analysis, offering tools to make sense of complex, dynamic networks in today"s data-driven world.
- Funding Agency
- Deutsche Forschungsgemeinschaft (DFG)
- Date From
- 01.09.2023
- Date To
- 31.07.2024
Recognition
Representations
I was elected to the board of the Young Irish Statistical Association (Y-ISA) in May 2025 and led the 2026 webinar on the "Role of statisticians in reproducibility", while contribing to the Y-ISA pub quiz at CASI 2026.
Awards and Honours
Postdoc Travel Award - Pennsylvania State University
Best Dissertation Award - Ludwig Maximilian University of Munich
Munich Center for Machine Learning (MCML) Certificate
Best Poster Award - DAGSTAT
Core-member of CAS Focus Group on Policies for the Prevention of Conflict
Member of LMU Mentoring Program
Best Master Thesis Award - Department of Statistics
Memberships
Irish Statistical Association
German Statistical Association (DStatG)
American Statistical Association