Uncovering individual and collective human dynamics from mobile phone records

Abstract

Novel aspects of human dynamics and social interactions are investigated by means of mobile phone data. Using extensive phone records resolved in both time and space, we study the mean collective behavior at large scales and focus on the occurrence of anomalous events. We discuss how these spatiotemporal anomalies can be described using standard percolation theory tools. We also investigate patterns of calling activity at the individual level and show that the interevent time of consecutive calls is heavy-tailed. This finding, which has implications for dynamics of spreading phenomena in social networks, agrees with results previously reported on other human activities.

16 pages, 7 figures; minor changes. To appear in J. Phys. A

References in corpus (10)

• Structure and tie strengths in mobile communication networks
• Quantifying social group evolution
• Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions
• Analysis of a large-scale weighted network of one-to-one human communication
• A system of mobile agents to model social networks
• The effects of spatial constraints on the evolution of weighted complex networks
• Impact of memory on human dynamics
• Scaling in the Inter-Event Time of Random and Seasonal Systems
• Irreversible growth of binary mixtures on small-world networks
• Nonequilibrium Opinion Spreading on 2D Small-World Networks

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