Abstract
Recent studies have increasingly turned to graph theory to model Realistic Contact Networks (RCNs) for characterizing propagation dynamics. Several of these studies have demonstrated that RCNs are best described as having exponential degree distributions. In this article, based on the mobile data gathered from in-vehicle wireless devices, we show that RCNs do not always have exponential degree distributions, especially in dynamic environments. On this basis, a model is designed to recognize the structure of networks. Based on the model, we investigate the impacts of network structure on disease dynamics that is an important empirical study to the propagation dynamics. The time-varying infected number R is the important parameter that is used to quantify the disease dynamics. In this study, the prediction accuracy for R is improved by utilizing realistic structural knowledge mined by our recognition model.
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Notes
- 1.
R is defined as the number of infected cases during an epidemic over time.
- 2.
In this study, the network with exponential degree distribution is named as “Exponential Network”.
- 3.
This subnetwork is obtained by a time-based sample. It is the contact network of this day, August 26th, 2014.
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Acknowledgments
This work is supported by Guangdong University of Petrochemical Technology’s Internal Project No. 2012RC106, Educational Commission of Guangdong Province, China Project No. 2013KJCX0131, Guangdong High-Tech Development Fund No. 2013B010401035, 2013 Special Fund of Guangdong Higher School Talent Recruitment, National Natural Science Foundation of China under Grant 61401107, 2013 Top Level Talents Project in “Sailing Plan of Guangdong Province”, and 2014 Guangdong Province Outstanding Young Professor Project.
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Chen, Y., Crespi, N., Shu, L., Myoung Lee, G. (2015). Reality Mining with Mobile Data: Understanding the Impact of Network Structure on Propagation Dynamics. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9531. Springer, Cham. https://doi.org/10.1007/978-3-319-27140-8_31
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DOI: https://doi.org/10.1007/978-3-319-27140-8_31
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