A dynamic model of time-dependent complex networks

Abstract

The characterization of the "most connected" nodes in static or slowly evolving complex networks has helped in understanding and predicting the behavior of social, biological, and technological networked systems, including their robustness against failures, vulnerability to deliberate attacks, and diffusion properties. However, recent empirical research of large dynamic networks (characterized by connections that are irregular and evolve rapidly) has demonstrated that there is little continuity in degree centrality of nodes over time, even when their degree distributions follow a power law. This unexpected dynamic centrality suggests that the connections in these systems are not driven by preferential attachment or other known mechanisms. We present a novel approach to explain real-world dynamic networks and qualitatively reproduce these dynamic centrality phenomena. This approach is based on a dynamic preferential attachment mechanism, which exhibits a sharp transition from a base pure random walk scheme.

8 pages, 6 figures; This is a substantial revision of the previous version

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• Opinion Dynamics on Networks under Correlated Disordered External Perturbations
• Impact of environmental changes on the dynamics of temporal networks
• Conceptual quantification of the dynamicity of longitudinal social networks
• Enhancing Information Dissemination in Dynamic Wireless Network using Stability and Beamforming
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