
Contents
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Conceptualizing Network Dynamics Conceptualizing Network Dynamics
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Network Change Processes Network Change Processes
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Nodal Effects Nodal Effects
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Dyadic Effects Dyadic Effects
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Endogenous Structure Endogenous Structure
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Modeling Network Dynamics Modeling Network Dynamics
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The Relational Event Framework The Relational Event Framework
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Stochastic Actor-Oriented Framework Stochastic Actor-Oriented Framework
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The Exponential Random Graph Framework The Exponential Random Graph Framework
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Model Selection Model Selection
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Empirical Example of Three Approaches Empirical Example of Three Approaches
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Model Statistics Model Statistics
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Attribute Effects: Age Attribute Effects: Age
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Dyadic Effects: Proximity Dyadic Effects: Proximity
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Endogenous Effects: Reciprocity Endogenous Effects: Reciprocity
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Endogenous Effects: Triadic Closure Endogenous Effects: Triadic Closure
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Modeling Strategy and Results Modeling Strategy and Results
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Baseline Models Baseline Models
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More Practical Models More Practical Models
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Extended Models Extended Models
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Subsequent Steps Subsequent Steps
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Outstanding Issues and Future Directions Outstanding Issues and Future Directions
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Notes Notes
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References References
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14 Modeling Network Dynamics
Get accessDavid R. Schaefer is Professor of Sociology at the University of California, Irvine. His research investigates the mechanisms responsible for network formation and change, with an empirical focus on networks in school and prison settings. In addition, he studies how social networks influence various outcomes related to human development and health-related behavior. He is the recipient of the 2012 Freeman Award for Distinguished Scholarship from the International Network for Social Network Analysis.
Christopher Steven Marcum is a Staff Scientist for Data Science Policy at the National Institute of Allergy and Infectious Diseases. His research has two arms: network methods development and understanding how health impacts network processes over the life course. He is the recipient of the 2015 Matilda White Riley Early Stage Investigator Award from the Office of Behavioral and Social Science Research at the National Institutes of Health for his work on intergenerational exchange from a network perspective.
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Published:15 December 2020
Cite
Abstract
Given that social networks are inherently dynamic phenomena, characterizing their structure, precursors, and consequences can be improved by methodologies that incorporate such dynamism. This chapter discusses several longitudinal network modeling approaches that seek to understand the process of network change, on one hand, and to predict future network states, on the other. These include the relational event model (REM), exponential random graph model (ERGM), and stochastic actor-oriented model (SAOM). These models focus on different temporal resolutions and differentiate instantaneous events from relations with longer durations, among other distinctions. The chapter identifies commonalities and unique features of each model, both conceptually and via an application to a longitudinal network dataset of dominance interactions within a herd of Eurasian red deer. Throughout, the chapter emphasizes each modeling framework’s assumptions, data requirements, and parameter and model interpretation.
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