Emergence of Bursts and Communities in Evolving Networks
Author Information
Author(s): Jo Hang-Hyun, Pan Raj Kumar, Kaski Kimmo
Primary Institution: Aalto University School of Science, Espoo, Finland
Hypothesis
How do community structure and bursty dynamics emerge together in a simple evolving weighted network model?
Conclusion
The study shows that the interplay of social interaction mechanisms leads to the emergence of heavy-tailed inter-event time distribution and Granovetter-type community structure.
Supporting Evidence
- The study reveals that social networks exhibit modular structures with strong intra-community links and weak inter-community links.
- The model successfully reproduces the heavy-tailed distribution of inter-event times observed in real-world data.
- The findings align with Granovetter's hypothesis regarding the strength of weak ties in social networks.
Takeaway
The way people interact in social networks can create groups and patterns of activity that are not random, but instead follow certain rules, making some connections stronger than others.
Methodology
The study uses numerical simulations of a network model incorporating social interaction mechanisms to analyze community structure and dynamics.
Limitations
The model may not fully capture the complexities of real-world social networks due to simplifications in the interaction processes.
Digital Object Identifier (DOI)
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