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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 2, 2016.
Abstract: The human mobility networks are vital infrastructure in recent social systems. Many efforts have been made to keep the healthy human mobility flows to maintain sustainable development of recent well-connected society. However, the inter-connectivity sometimes raises unintended diffusion and amplification of the intrinsic risks, which is difficult to forecast because of the complexity of the underlying networks. Therefore, it is believed that modeling and simulation of the risk diffusion in the human mobility networks are suggestive and meaningful. Also, recent improvement of usability of individual-level human mobility data and capabilities of high-performance computing technologies enable us to employ the data-driven approaches. In this paper, the risk diffusion dynamics is modeled based on the SIS epidemic model and the vulnerability index is defined to quantify the node-level easiness of suffering risks. We also conduct the link removal test to find the better risk mitigation methods.
Kiyotaka Ide, Hiroshi Sato, Tran Quang Hoang Anh and Akira Namatame, “Risk Diffusion Modeling and Vulnerability Quantification on Japanese Human Mobility Network from Complex Network Analysis Point of View” International Journal of Advanced Computer Science and Applications(IJACSA), 7(2), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070245
@article{Ide2016,
title = {Risk Diffusion Modeling and Vulnerability Quantification on Japanese Human Mobility Network from Complex Network Analysis Point of View},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070245},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070245},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {2},
author = {Kiyotaka Ide and Hiroshi Sato and Tran Quang Hoang Anh and Akira Namatame}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.