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Temporal Exponential Random Graph Models for signed networks and Relational Event Models for spurious events
Host: Prof. Ernst Wit
USI Campus EST, room D3.01, Sector D
University of Munich
Substantive research in the Social Sciences regularly investigates signed networks, where edges between actors are either positive or negative. For instance, schoolchildren can be friends or rivals, just as countries can cooperate or fight each other. This research often builds on structural balance theory, one of the earliest and most prominent network theories, making signed networks one of the most frequently studied matters in social network analysis. While the theorization and description of signed networks have thus made significant progress, the inferential study of tie formation within them remains limited in the absence of appropriate statistical models. We fill this gap by proposing the Signed Exponential Random Graph Model (SERGM), extending the well-known Temporal Exponential Random Graph Model (TERGM) to networks where ties are not binary but negative or positive if a tie exists. Our empirical application uses the SERGM to analyze cooperation and conflict between countries within the international state system.
At the same time, relational event models are an increasingly popular model for studying relational structures; hence the reliability of large-scale event data collection becomes more and more important. Automated or human-coded events often suffer from non-negligible false-discovery rates in event identification. And most sensor data is primarily based on actors' spatial proximity for predefined time windows; hence, the observed events could relate either to a social relationship or random co-location. Both examples imply spurious events that may bias estimates and inference. We propose the Relational Event Model for Spurious Events (REMSE), an extension to existing approaches for interaction data. The model provides a flexible solution for modeling data while controlling for spurious events. We employ this model to combat events from the Syrian civil war.
Biography: Cornelius Fritz is a Ph.D. student in statistics under the supervision of Göran Kauermann. In this context, his research mainly revolves around analyzing dynamic networks to answer questions posed within substantive sciences, e.g., Political Science and Sociology, through novel data analysis techniques that combine statistical and machine learning thinking. He is also an active member of the CODAG (COVID-19 Data Analysis Group), where he focuses on analyzing the interplay of mobility patterns and COVID-19 infections.
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