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Quantifying agent impacts on contact sequences in social interactions
Mark M Dekker1,2, Tessa F Blanken3, Fabian Dablander3
1Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC, Utrecht, The Netherlands. m.m.dekker@uu.nl.
Scientific Reports
|March 4, 2022
Summary
Understanding social behavior is key to controlling the spread of diseases like SARS-CoV-2 and misinformation. This study introduces a new metric, contact sequence centrality, to identify individuals who may be behavioral super-spreaders.
Area of Science:
- Network Science
- Epidemiology
- Social Behavior Analysis
Background:
- Human social behavior significantly influences the spread of pathogens (e.g., SARS-CoV-2) and information (e.g., fake news).
- Contact networks, formed by social interactions, are crucial for understanding transmission dynamics.
- Analyzing temporal network data requires methods that account for the dynamic nature of interactions.
Purpose of the Study:
- To develop a novel method for quantifying individual impact on spreading phenomena within temporal social networks.
- To introduce a new metric, contact sequence centrality, to identify potential 'behavioral super-spreaders'.
- To assess the effectiveness of this new metric compared to traditional static and temporal network measures.
Main Methods:
- Event mapping was used to transform temporal network data of agent contacts.
- A novel metric, contact sequence centrality, was defined to quantify an individual's impact on contact sequences.
- The method was applied to social interaction data from an art fair in Amsterdam.
Main Results:
- Contact sequence centrality effectively quantifies an individual's behavioral potential for spreading.
- The new metric allows for ranking individuals and identifying potential behavioral super-spreaders.
- Traditional network metrics (static and temporal) showed reduced resemblance to contact sequence centrality, especially at longer time scales.
Conclusions:
- Accounting for the sequential nature of contacts is crucial for accurate analysis of social interactions and spreading phenomena.
- Contact sequence centrality offers a valuable tool for understanding and potentially intervening in disease and information spread.
- The findings underscore the limitations of static network analysis in dynamic social contexts.
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