산업경영시스템학회지 (Journal of Korean Society of Industrial and Systems Engineering)

  • 제46권4호
  • /
  • Pages.53-62
  • /
  • 2023
  • /
  • 2005-0461(pISSN)
  • /
  • 2287-7975(eISSN)
한국산업경영시스템학회 (Society of Korea Industrial and System Engineering)
권호 표지
DOI QR코드

DOI QR Code

핵심 노드 선정을 위한 네트워크 기반 최적화 모델

A Network-based Optimization Model for Effective Target Selection

  • 이진호 (해군사관학교 국방경영학과) ;
  • 이기현 (연세대학교 연세경영연구소)
  • Jinho Lee (Department of Defense Management, Korea Naval Academy) ;
  • Kihyun Lee (Yonsei Business Research Institute, Yonsei University)
  • 투고 : 2023.08.27
  • 심사 : 2023.10.04
  • 발행 : 2023.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Effects-Based Operations (EBO) refers to a process for achieving strategic goals by focusing on effects rather than attrition-based destruction. For a successful implementation of EBO, identifying key nodes in an adversary network is crucial in the process of EBO. In this study, we suggest a network-based approach that combines network centrality and optimization to select the most influential nodes. First, we analyze the adversary's network structure to identify the node influence using degree and betweenness centrality. Degree centrality refers to the extent of direct links of a node to other nodes, and betweenness centrality refers to the extent to which a node lies between the paths connecting other nodes of a network together. Based on the centrality results, we then suggest an optimization model in which we minimize the sum of the main effects of the adversary by identifying the most influential nodes under the dynamic nature of the adversary network structure. Our results show that key node identification based on our optimization model outperforms simple centrality-based node identification in terms of decreasing the entire network value. We expect that these results can provide insight not only to military field for selecting key targets, but also to other multidisciplinary areas in identifying key nodes when they are interacting to each other in a network.

키워드

과제정보

This work was supported by the Korea Naval Institute for Ocean Research at the Republic of Korea Naval Academy.

참고문헌

  1. Albert, R., Jeong, H., and Barabasi, A.L., Internet: Diameter of the world-wide web, Nature, 1999, Vol.401, pp.130-131. https://doi.org/10.1038/43601
  2. Borgatti, S.P., Everett, M.G., and Freeman, L.C., Ucinet 6 for Windows: Software for Social Network Analysis, Analytic Technologies, 2002.
  3. Dickerson, J.A. and Kosko, B., Virtual Worlds as Fuzzy Cognitive Maps, Prentice Hall, 1997.
  4. Duczynski, G., Effects-Based Operations: A Guide for Practitioners, Proceedings of Command and Control Research and Technology Symposium, 2004, San Diego, CA.
  5. Freeman, L.C., Borgatti, S.P., and White, C.R., Centrality in valued graphs: A measure of betweenness based on network flow, Social Networks, 1991, Vol. 13, pp. 141-154. https://doi.org/10.1016/0378-8733(91)90017-N
  6. Isaiah, G.A. and Sun, Y., A novel approach of closeness centrality measure for voltage stability analysis in an electric power grid, International Journal of Emerging Electric Power Systems, 2020, Vol. 21, No. 3, p. 20200013.
  7. Joint Warfighting Center, Joint Doctrine Series: Pamphlet 7, Operational Implications of Effects-Based Operations, U.S. Joint Forces Command, 2004.
  8. Katz, L., A new status index derived from sociometric index, Psychometrika, 1953, Vol. 18, pp. 39-43. https://doi.org/10.1007/BF02289026
  9. Kitsak, M., Gallos, L.K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H.E., and Makse, H.A., Identification of influential spreaders in complex networks, Nature Physics, 2010, Vol. 6, pp. 888-893. https://doi.org/10.1038/nphys1746
  10. Kosko, B., Fuzzy cognitive maps, International Journal of Man-Machine Studies, 1986, Vol. 24, pp. 65-75. https://doi.org/10.1016/S0020-7373(86)80040-2
  11. Koulouriotis, D.E., Diakoulakis, I.E., Emiris, D.M., and Zopounidis, C.D., Development of dynamic cognitive networks as complex systems approximators: Validation in financial time series, Applied Soft Computing, 2004, Vol. 5, pp. 157-179. https://doi.org/10.1016/j.asoc.2004.06.004
  12. Lai, Q. and Zhang, H.H., Analysis of identification methods of key nodes in transportation network, Chinese Physics B, 2022, Vol. 31, No. 6, p. 068905.
  13. Lee, S., Centrality-based ambulance dispatching for demanding emergency situations, Journal of the Operational Research Society, 2013, Vol. 64, pp. 611-618. https://doi.org/10.1057/jors.2012.72
  14. Negre, C.F.A., Morzan, U.N., Hendrickson, H.P., Pal, R., Lisi, G.P., Loria, P., Rivalta, I., Ho, J., and Batista, V.S., Eigenvector centrality for characterization of protein allosteric pathways, Proceedings of the National Academy of Sciences, 2018, Vol. 115, No. 52, pp. E12201-E12208. https://doi.org/10.1073/pnas.1810452115
  15. Sabidussi, G., The centrality index of a graph, Psychometrika, 1996, Vol. 31, pp. 581-603. https://doi.org/10.1007/BF02289527
  16. Scott, J., Social network analysis: A handbook, Sage Publications, 2007.
  17. Smith, E.R., Effects based operations: Applying network centric warfare to peace, crisis, and war, DOD-CCRP, Washington DC, 2006.
  18. Sullivan, D., What is Google PageRank? A Guide for Searchers & Webmasters, Search Engine Land, 2007.
  19. Umstead, R. and Denhard, D.R., Viewing the center of gravity through the prism of effects-based operations, Military Review, 2006, Sept-Oct, pp. 90-95.
  20. Wagenhals, L.W. and Levis, A.H., Modeling Support of Effects-Based Operations in War Games, Proceedings of Command and Control Research and Technology Symposium, 2002, Monterey, CA.
  21. Wagenhals, L.W., Levis, A.H., and Haider, S.P., Planning, execution and assessment of effects-based operations (EBO), Technical Report, Air Force Research Laboratory / IFSA, 2006.
  22. Wang, F., Sun, Z., Gan, Q., Fan, A., Shi, H. and Hu, H., Influential node identification by aggregating local structure information, Physica A: Statistical Mechanics and Its Applications, 2022, Vol. 593, No. 1, p. 126885.
  23. Wasserman, S.J. and Faust, K., Social network analysis: Methods and application, Cambridge University Press, 1994.
  24. Yaman, D. and Polat, S., A fussy cognitive map approach for effect-based operations: An illustrative case, Information Sciences, 2009, Vol.179, pp.382-403. https://doi.org/10.1016/j.ins.2008.10.013
  25. Yang, H. and An, S., Critical nodes identification in complex networks, Symmetry, 2020, Vol. 12, p. 123.