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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Entropy Measures of Human Communication Dynamics.
Marcin Kulisiewicz1, Przemysław Kazienko2, Boleslaw K Szymanski3
1Wroclaw University of Science and Technology, Department of Computational Intelligence, Wroclaw, 50-370, Poland. marcin.kulisiewicz@pwr.edu.pl.
Scientific Reports
|October 26, 2018
Summary
Human communication patterns are unique and distinct from random interactions. New entropy measures reveal evolving sociological processes in dynamic communities over time.
Area of Science:
- Social network analysis
- Information theory
- Sociology
Background:
- Human communication is often modeled as a temporal social network.
- Evaluating the uniqueness of communication patterns is a key area of study.
Purpose of the Study:
- To introduce novel entropy-based measures for analyzing human communication dynamics.
- To quantify the distinctiveness of human communication within temporal social networks.
- To observe sociological processes in dynamic communities.
Main Methods:
- Development of new entropy-based measures.
- Analysis of human communication dynamics as event sequences in temporal social networks.
- Comparison of real-world datasets with random interaction series.
Main Results:
- Real human contact events significantly differ from random interaction series.
- Human communication distinctiveness increases over time.
- The proposed entropy measures effectively capture sociological processes.
Conclusions:
- Human communication exhibits inherent uniqueness not found in random interactions.
- Entropy measures provide a quantitative method to study social dynamics.
- These measures can reveal underlying sociological processes in evolving communities.
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