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Communication activity in a social network: relation between long-term correlations and inter-event clustering
Diego Rybski1, Sergey V Buldyrev, Shlomo Havlin
1Levich Institute and Physics Department, City College of New York, NY 10031, USA.
Scientific Reports
|August 10, 2012
Summary
Individual user activity in social networks shows clustering, but community-level interactions reveal true emergent long-term correlations. This suggests complex communication patterns drive collective behavior in social systems.
Area of Science:
- Social network analysis
- Statistical physics of complex systems
- Human communication dynamics
Background:
- Human communication in social networks exhibits statistical laws like correlations and temporal clustering.
- Previous research identified long-term correlations in individual user activity within social communities.
Purpose of the Study:
- To investigate the origins of temporal clustering and long-term persistence in social community activity.
- To differentiate between individual user activity patterns and emergent collective behavior.
Main Methods:
- Analysis of inter-event time distributions for individual users.
- Examination of long-term correlations in the activity of entire social communities.
- Comparison of individual-level clustering with system-level emergent properties.
Main Results:
- Individual user activity correlations stem from clustered inter-event times (power-law distribution).
- Community-level activity displays true long-term correlations, independent of individual inter-event time distributions.
- These emergent correlations suggest non-trivial communication patterns within the social network.
Conclusions:
- Collective behavior in social networks is an emergent property not solely explained by individual user activity patterns.
- The findings highlight the importance of network structure and communication patterns in shaping emergent phenomena.
- Understanding these emergent properties is crucial for comprehending large-scale social dynamics.
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