Stability in flux: community structure in dynamic networks.
John Bryden1, Sebastian Funk, Nicholas Geard
1School of Biological Sciences, Royal Holloway, University of London, Egham TW20 0EX, UK. john.bryden@rhul.ac.uk
Journal of the Royal Society, Interface
|December 3, 2010
Summary
Dynamic complex networks, crucial for biological and social systems, maintain stable community structures. Node behavior and state adoption drive this emergent modularity, even as groups change.
Area of Science:
- Network Science
- Complex Systems
- Computational Social Science
Background:
- Complex networks describe many biological, social, and technological systems.
- These networks are dynamic, with changing connections (edges) and states.
- Understanding network dynamics is key to understanding system structure.
Purpose of the Study:
- To characterize how individual node dynamics generate stable aggregate behaviors in coevolving networks.
- To analyze network modularity based on node state and topology.
- To investigate the impact of fixed versus dynamic node states on group stability.
Main Methods:
- Building upon existing models of coevolving networks.
- Focusing on endogenous group formation based on shared node properties (state).
- Analytical quantification of network modularity equilibrium and group size distribution.
Main Results:
- Network modularity based on node state can be comparable to topological modularity under certain conditions.
- Fixed node states lead to a stable, analytically quantifiable network modularity equilibrium.
- Dynamic node states (adoption from neighbors) result in a stable equilibrium of group size distribution, despite changing group composition.
Conclusions:
- Dynamic network processes can generate and maintain stable community structures observed in real-world systems.
- Node state dynamics play a significant role in emergent network organization.
- The study provides a framework for understanding stability in dynamic complex systems.
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