40 citations · 126 across the 13 of their papers we have counts for
14 papers
A Counting Process View of Relational Event Models: Practical Asymptotics
Cornelius Fritz, Alexander Fuchs-Kreiss
Relational Event Models (REMs) provide a rigorous framework for analyzing dyadic interactions observed in continuous time, capturing history-dependent dynamics such as triadic clos…
Scalable Signed Exponential Random Graph Models under Local Dependence
Marc Schalberger, Cornelius Fritz
Traditional network analysis focuses on binary edges, while real-world relationships are more nuanced, encompassing cooperation, neutrality, and conflict. The rise of negative edge…
Scalable Durational Event Models: Application to Physical and Digital Interactions
Cornelius Fritz, Riccardo Rastelli, Michael Fop +1
Durable interactions are increasingly observed in social network analysis with precise timestamps. Phone and video calls, for instance, are events to which a specific duration can…
How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression
Lucas Kook, Chris Kolb, Philipp Schiele +8
Neural network representations of simple models, such as linear regression, are being studied increasingly to better understand the underlying principles of deep learning algorithm…
Dependence matters: Statistical models to identify the drivers of tie formation in economic networks
Giacomo De Nicola, Cornelius Fritz, Marius Mehrl +1
Networks are ubiquitous in economic research on organizations, trade, and many other areas. However, while economic theory extensively considers networks, no general framework for…
Exponential Random Graph Models for Dynamic Signed Networks: An Application to International Relations
Cornelius Fritz, Marius Mehrl, Paul W. Thurner +1
Substantive research in the Social Sciences regularly investigates signed networks, where edges between actors are either positive or negative. For instance, schoolchildren can be…
Modelling the large and dynamically growing bipartite network of German patents and inventors
Cornelius Fritz, Giacomo De Nicola, Sevag Kevork +2
We analyse the bipartite dynamic network of inventors and patents registered within the main area of electrical engineering in Germany to explore the driving forces behind innovati…
Statistical modelling of COVID-19 data: Putting Generalised Additive Models to work
Cornelius Fritz, Giacomo De Nicola, Martje Rave +5
Over the course of the COVID-19 pandemic, Generalised Additive Models (GAMs) have been successfully employed on numerous occasions to obtain vital data-driven insights. In this pap…
All that Glitters is not Gold: Relational Events Models with Spurious Events
Cornelius Fritz, Marius Mehrl, Paul W. Thurner +1
As relational event models are an increasingly popular model for studying relational structures, the reliability of large-scale event data collection becomes more and more importan…
Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany
Cornelius Fritz, Emilio Dorigatti, David Rügamer
During 2020, the infection rate of COVID-19 has been investigated by many scholars from different research fields. In this context, reliable and interpretable forecasts of disease…
The Role of Governmental Weapons Procurements in Forecasting Monthly Fatalities in Intrastate Conflicts: A Semiparametric Hierarchical Hurdle Model
Cornelius Fritz, Marius Mehrl, Paul W. Thurner +1
Accurate and interpretable forecasting models predicting spatially and temporally fine-grained changes in the numbers of intrastate conflict casualties are of crucial importance fo…
On the Interplay of Regional Mobility, Social Connectedness, and the Spread of COVID-19 in Germany
Cornelius Fritz, Göran Kauermann
Since the primary mode of respiratory virus transmission is person-to-person interaction, we are required to reconsider physical interaction patterns to mitigate the number of peop…
Separable and Semiparametric Network-based Counting Processes applied to the International Combat Aircraft Trades
Cornelius Fritz, Paul W. Thurner, Göran Kauermann
We propose a novel tie-oriented model for longitudinal event network data. The generating mechanism is assumed to be a multivariate Poisson process that governs the onset and repet…
Tempus Volat, Hora Fugit -- A Survey of Tie-Oriented Dynamic Network Models in Discrete and Continuous Time
Cornelius Fritz, Michael Lebacher, Göran Kauermann
Given the growing number of available tools for modeling dynamic networks, the choice of a suitable model becomes central. The goal of this survey is to provide an overview of tie-…