Dr. Cornelius Fritz

• 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, 2025Journal Article, 2025, URL
• 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, 2022Journal Article, 2022, DOI
• 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--21Journal Article, 2021, DOI
• 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?, 2024Conference Paper, 2024
• 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, 2025Journal Article, 2025, DOI
• 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 - 363Journal Article, 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 - 576Journal Article, 2023
• 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 - 31Journal Article, 2023
• 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), 2023Journal Article, 2023
• 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), 2022Journal Article, 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 - 18Journal Article, 2022
• 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 - 424Journal Article, 2022
• 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 - 299Journal Article, 2020
• 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 - 402Journal Article, 2020
• 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, 2023Journal Article, 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 - 5Journal Article, 2023
• Fritz Cornelius, Dworschak Christoph, Mehrl Marius, Predicting uncertainty in stages: Using a semiparametric hierarchical hurdle model for predicting distributions of conflict fatalities , 2024, -Miscellaneous, 2024
• 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 - 502Journal Article, 2022
• Fritz Cornelius, Schweinberger Michael, Bhadra Subhankar, Hunter David R., A regression framework for studying relationships among attributes under network interference , ArXiv e-prints , 2024Journal Article, 2024
• Espinosa-Rada Alejandro, Lerner Jürgen, Fritz Cornelius, Socio-cognitive networks between researchers , ArXiv e-prints , 2024Journal Article, 2024
• Fritz Cornelius, Georg Co-Pierre, Mele Angelo, Schweinberger Michael, A strategic model of software dependency networks , ArXiv e-prints , 2024Journal Article, 2024

• 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

Large and complex data theory, Statistics not elsewhere classified, Optimisation, Spatial statistics, Digital sociology, Statistics, Stochastic analysis and modelling, Computational statistics, Sociological methodology and research methods, Applied statistics, Biostatistics, Statistical data science, Sociology of knowledge, Statistical theory,

• Best Master Thesis Award - Department of Statistics 2019
• Core-member of CAS Focus Group on Policies for the Prevention of Conflict 2022
• Best Dissertation Award - Ludwig Maximilian University of Munich 2023
• Member of LMU Mentoring Program 2022
• Munich Center for Machine Learning (MCML) Certificate 2023
• Postdoc Travel Award - Pennsylvania State University 2023
• Best Poster Award - DAGSTAT 2022

• German Statistical Association (DStatG)
• American Statistical Association