Journal of Korean Society of Rural Planning (농촌계획)

Korean Society of Rural Planning (한국농촌계획학회)
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Development of Modeling to Find the Hub Nodes on Growing Scale-free Network based on Stochastic Community Bridge Node Finder

확장하는 Scale-free 네트워크에서의 허브노드 도출을 위한 Stochastic Community Bridge Node Finder 개발

  • Eun, Sang-Kyu (Institute of Green Bio Science and Technology, Seoul National University, Department of Agricultural Engineering, Chungnam National University) ;
  • Kim, Soo-Jin (Institute of Green Bio Science and Technology, Seoul National University, Department of Agricultural Engineering, Chungnam National University) ;
  • Bae, Seung-Jong (Institute of Green Bio Science and Technology, Seoul National University, Department of Agricultural Engineering, Chungnam National University) ;
  • Kim, Dae-Sik (Institute of Green Bio Science and Technology, Seoul National University, Department of Agricultural Engineering, Chungnam National University)
  • 은상규 (서울대학교 그린바이오과학기술연구원, 충남대학교 농업생명과학대학) ;
  • 김수진 (서울대학교 그린바이오과학기술연구원, 충남대학교 농업생명과학대학) ;
  • 배승종 (서울대학교 그린바이오과학기술연구원, 충남대학교 농업생명과학대학) ;
  • 김대식 (서울대학교 그린바이오과학기술연구원, 충남대학교 농업생명과학대학)
  • Received : 2016.10.27
  • Accepted : 2016.11.22
  • Published : 2017.02.28
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Abstract

The community bridge node finder, based on the stochastic method of network analysis, can compute hubs spot, which would enable the use of network structures with limited information. However, applying this node finder to heterogeneity networks, which are efficient to analyze the main farm complex in fields and the spread of infectious disease, is difficult. These problems, The most connected point that is called hub is often a major role in the heterogeneity network. In this study, we therefore improved the community bridge node finder to enable it to be applied to heterogeneity networks. We attempted to calculate the bridge node quantitatively by using the modularity of cohesion analysis method and the community bridge node finder. Application of the improved method to the HPAI(Highly Pathogenic Avian Influenza) spread in Korea 2008 produced a quarantine coefficient that was 4 - 37% higher than the quarantine coefficient obtained with the centrality method for the first 14 days after the HPAI outbreak. We concluded that the improved method has the ability to successfully calculate the bridge node in heterogeneity networks based on network structures with scant information, such as those describing the spread of infectious disease in domestic animals. And Our method should be capable to find main farm complex in fields.

네트워크를 분석하는 방법인 stochastic 기반의 community bridge node finder는 네트워크 구조의 일부 정보만을 이용하여 중심지 및 확산 경로의 유추가 가능하다. 그러나, heterogeneity 네트워크에 적용하기 어려운 단점을 보유하고 있어, 본 연구에서는 이를 개선하고자 하였다. heterogeneity 네트워크에 적용할 수 있는 community bridge node finder 방법을 개발하기 위해 네트워크의 modularity를 계측하는 방법을 적용하였으며, 개선된 방법을 통해 community bridge node를 정량적으로 평가할 수 있어 heterogeneity 네트워크의 분석이 가능하였다. 농촌계획분야 정보의 경우, 대부분 자료의 불확실성이 존재하며 무결성이 떨어지는 특성을 가지고 있는 바 본 연구에서 적용한 전염병 확산 예측 뿐만 아니라 개선된 방법을 활용할 경우 주산지 정비 거점 분석 등 다양한 형태로 이용될 수 있을 것으로 기대된다.

Keywords

References

  1. Anderson, R. M., May, R. M., Boily, M. C., Garnett, G. P., & Rowley, J. T., 1991, The spread of HIV-1 in Africa: sexual contact patterns and the predicted demographic impact of AIDS. Nature, 352: 581-589. https://doi.org/10.1038/352581a0
  2. Barabasi, A. L., Albert, R., & Jeong, H., 2000, Scale-free characteristics of random networks: the topology of the world-wide web. Physica A, 281: 69-77. https://doi.org/10.1016/S0378-4371(00)00018-2
  3. Danon, L., Ford, A. P., House, T., Jewell, C. P., Keeling, M. J., Roberts, G. O., Ross, J. V., & Vernon, M. C., 2011, Networks and the epidemiology of infectious disease. Interdisciplinary Perspectives on Infectious Diseases, 284909.
  4. Eames, K. T. D., 2008, Modelling disease spread through random and regular contacts in clustered populations. Theoretical Population Biology, 73: 104-111. https://doi.org/10.1016/j.tpb.2007.09.007
  5. Fraser, C., Riley, S., Anderson, R. M., & Ferguson, N. M., 2004, Factors that make an infectious disease outbreak controllable. Procedures of the National Academy Of Sciences USA, 101: 6146-6151. https://doi.org/10.1073/pnas.0307506101
  6. Gong, K., Tang, M., Hui, P. M., Zhang, H. F., Younghae, D., & Lai, Y. C., 2013, An efficient immunization strategy for community networks. PLOS. Computational biology, 8(12): 1-11. e83489.
  7. Hayama, Y., Muroga, N., Nishida, T., Kobayashy, S., & Tsutsui, T., 2012, Risk factors for local spread of foot-and-mouth disease, 2010 epidemic in Japan. Research in Veterinary Science, 93: 631-635. https://doi.org/10.1016/j.rvsc.2011.09.001
  8. Haydon, D. T., Chase, M., Shaw, D. J., Matthews, L., Friar, J. K., Wilesmith, J., & Woolhouse, M. E. J. , 2003, The construction and analysis of epidemic trees with reference to the 2001 UK foot-and-mouth outbreak. Procedures of the Royal Society, 270: 121-127. https://doi.org/10.1098/rspb.2002.2191
  9. Hosseini, P. R., Fuller, T., Harrigan, R., Zhao, D., Arriola, C. S., Gonzalez, A., Miller, M. J., Xiao, X., Smith, T. B., Jones, J. H., & Daszak, P., 2013, Metapopulation dynamics enable persistence of influenza A, including A/H5N1, in poultry. PLOS. Computational biology, 8(12): e80091.
  10. House, T., Davies, G., Danon, L., & Keeling, M. J., 2009, A Motif-Based Approach to Network Epidemics. Bulletin of Mathematical Biology, 71(7): 1693-1706. https://doi.org/10.1007/s11538-009-9420-z
  11. Keeling, M. J. & Eames, K. T. D., 2005, Networks and Epidemic Models. Journal of the Royal Society Interface, 2: 295-307. https://doi.org/10.1098/rsif.2005.0051
  12. Keeling, M. J. & Rohani, P., 2008, Modeling Infectious Diseases. Princeton: Princeton University Press.
  13. Lee, H. J., Suh, K., Jung, N. S., Lee, I. B., Seo, I. H., Moon, O. K., & Lee, J. J., 2014, Prediction of the spread of highly pathogenic avian influenza using a multifactor network: Part 2 - Comprehensive network analysis with direct/indirect infection route. Biosystems Engineering, 118: 115-127. https://doi.org/10.1016/j.biosystemseng.2013.11.009
  14. Lloyd, A. L. & May, R. M., 2001, How viruses spread among computers and people. Science, 292: 1316-1317. https://doi.org/10.1126/science.1061076
  15. Riley, S. et al., 2003, Transmission dynamics of the etiological agent of SARS in Hong Kong: impact of public health interventions. Science, 300: 1961-1966. https://doi.org/10.1126/science.1086478
  16. Salathe, M. & Jones J. H., 2010, Dynamics and Control of Diseases in Networks with Community Structure. PLOS. Computational biology, 6(4): e1000736. https://doi.org/10.1371/journal.pcbi.1000736
  17. Shu, P., Tang, M., Gong, K., & Liu, Y., 2012, Effects of weak ties on epidemic predictability on community networks. Chaos, 22: 043124. https://doi.org/10.1063/1.4767955
  18. Smieszek, T., Fiebig, L., & Scholz, R. W., 2008, Models of epidemics: when contact repetition and clustering should be included. Theoretical Biology and Medical Modelling, 6(11): 1-15.
  19. Xu, Z., Zu, Z., Zheng, T., Zhang, W., Xu, Q., & Liu, J., 2010, Comparative analysis of the effectiveness of three immunization strategies in controlling disease outbreaks in realistic social networks. PLOS. Computational biology, 9(5): e95911.