Journal of Digital Contents Society (디지털콘텐츠학회 논문지)

Digital Contents Society (한국디지털콘텐츠학회)
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Generation of Dynamic Sub-groups for Social Networks Analysis

소셜 네트워크 분석을 위한 동적 하위 그룹 생성

  • 이현진 (숭실사이버대학교 컴퓨터정보통신학과)
  • Received : 2013.02.25
  • Accepted : 2013.03.22
  • Published : 2013.03.31
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Abstract

Social network analysis use the n nodes with l connections. About dozens or hundreds number of nodes are reasonable for social network analysis to the entire data. Beyond such number of nodes it will be difficult to analyze entire data. Therefore, it is necessary to separate the whole social networks, a method that can be used at this time is Clustering. You will be able to easily perform the analysis of the features of social networks and the relationships between nodes, if sub-group consists of all the nodes by Clustering. Clustering algorithm needs the interaction with the user and computer because it is need to pre-set the number of sub-groups. Sub-groups generated like this can not be guaranteed optimal results. In this paper, we propose dynamic sub-groups creating method using the external community association. We compared with previous studies by the number of sub-groups and sub-groups purity standards. Experimental results show the excellence of the proposed method.

소셜 네트워크 분석은 1개의 연결을 가지는 n개의 노드를 대상으로 한다. 노드 수 n이 수십 또는 수백 정도의 소셜 네트워크를 분석할 때는 전체 데이터를 대상으로 분석이 가능하지만, 그 이상이 되면 육안으로 분석하기는 어렵다. 따라서 전체 소셜 네트워크를 분리할 필요가 있는데 이 때 사용할 수 있는 방법이 군집화이다. 군집화를 통해 전체 노드를 하위 그룹으로 구성하면, 소셜 네트워크의 특징 분석이나 노드간의 관계 분석을 쉽게 수행할 수 있게 된다. 군집화 기법은 하위 그룹의 개수를 미리 설정해야 하기 때문에 사용자와의 상호 작용이 필요하고, 이렇게 생성된 하위 그룹이 최적의 결과라는 것을 보증할 수 없다. 본 논문에서는 외부 커뮤니티 연관도를 활용한 동적인 하위 그룹 생성 방법을 제안한다. 발견된 하위 그룹의 개수와 하위 그룹 순도를 기준으로 기존의 연구들과 비교하였고, 실험 결과 제안하는 방법의 우수성을 확인할 수 있었다.

Keywords

References

  1. C.T. Butts, "Social network analysis:A methodological introduction", Asian Journal of Social Psychology, Vol. 11, pp. 13-41, 2008. https://doi.org/10.1111/j.1467-839X.2007.00241.x
  2. Eun-Young Kang, and Kee-Young Kwahk, "Managing Duplicate Memberships of Websites:An Approach of Social Network Analysis," Journal of Intelligence and Information Systems, Vol. 17, No. 1, pp. 153-169, 2011.
  3. Seong-Hee Kim, and Rho-Sa Chang, "The Studyon the Research Trend of Social Network Analysisand the its Applicability to Information Science", Journal of Korea Society for Information Management, Vol. 27, No. 4, pp. 71-87, 2010. https://doi.org/10.3743/KOSIM.2010.27.4.071
  4. Hyunjin Lee, and Taechang Jee, "Social Networks Analysis using External Community Relationship", Journal of Digital Contents Society, Vol. 12, No. 1, pp. 69-75, 2011. https://doi.org/10.9728/dcs.2011.12.1.069
  5. Jong Hak Park, Yoon Ho Cho, and Jae Kyeong Kim, "Social Network:A Novel Approach to New Customer Recommendations", Journal of Intelligence and Information Systems, Vol. 15, No. 1, pp. 123-140, 2009.
  6. Hyoung-Do Kim, "Collaborative Filtering by Consistency Based Trust Definition", Journal of Society fore-Business Studies, Vol. 14, No. 1, pp. 1-11, 2009.
  7. Seung-Hoon Lee, Ji-Hyeok Kim, Heung-Nam Kim, and Geun-Sik Jo, "Inferring and Visualizing Semantic Relationships in Web-based Social Network", Journal of Intelligence and Information Systems, Vol.15, No. 1, pp. 87-102, 2009.
  8. M. Girvan and M.E.J. Newman, "Community structure in social and biological networks", Proceedings of the National Academy of Science, Vol. 99, No.12, pp. 7821-7826, 2002. https://doi.org/10.1073/pnas.122653799
  9. M.E.J. Newman, "Detecting community structure innetworks", Eur. Phys. J. B., Vol. 38, No. 2, pp. 321-330, 2004. https://doi.org/10.1140/epjb/e2004-00124-y
  10. Richard Freeman, "Topological Tree Clustering ofSocial Network Search Results" in Proceedings ofthe Eight International Conference on ]Lecture Notes in Computer Science (LNCS 4481), Springer, pp.760-769, 2007.
  11. Romain Boulet, Bertrand Jouvea, Fabrice Rossi, Nathalie Villa, "Batch kernel SOM and related Laplacian methods for social network analysis, "Neurocomputing, Vol. 71, PP. 1257-1273, 2008. https://doi.org/10.1016/j.neucom.2007.12.026
  12. Yong-Yeol Ahn, James P. Bagrow, Sune Lehmann, " Link communities reveal multiscale complexity innetworks, "nature, vol. 466, pp.761-764. 2010. https://doi.org/10.1038/nature09182
  13. Yoonseip Kang, and Seungjin Choi, "Social Network Analysis using Common Neighborhood Subgraph Density," Journal of KIISE:Computing Practices andLetters, Vol. 16, No. 4, pp. 432-436, 2010.
  14. C. Chen, "Visualizing Semantic Spaces and Author Co-Citation Networks in Digital Libraries," Information Processing Management, Vol. 35, No. 3, pp. 401-420, 1999. https://doi.org/10.1016/S0306-4573(98)00068-5
  15. F. Kerschbaum and A. Schaad, "Privacy-Preserving Social Network Analysis for Criminal Investigations," In WPES, Alexandria, Virginia, 2008.
  16. P.O. Wennerberg, "Ontology Based Knowledge Discovery in Social Networks," Final Report, JRC Joint Research Center, 2005.
  17. D. Joshi and D. GaticaPerez, "Discovering Groupsof People in Google News," Proceedings of the 1st ACM international workshop on Human-centered multimedia, pp. 55-64, 2006.
  18. Hyunjin Lee, and Taechang Jee, "A Study on Optimizing the Number of Clusters using External Cluster Relationship Criterion", Journal of Digital Contents Society, Vol. 12, No. 3, pp. 339-345, 2011. https://doi.org/10.9728/dcs.2011.12.3.339
  19. P. Velardi, R. Navigli, A. Cucciarelli and F. D'Antonio, "A New Content-Based Model for Social Network Analysis," Proceedings of the 2008 IEEE International Conference on Semantic Computing, pp. 18-25, 2008.
  20. C. Zhou, D. Frankowski, P. Ludford, S. Shekhar andL. Terveen, "Discovering Personal Gazetterrs:AnInteractive Clustering Approach," Proceedings of ACM GIS, 2004.
  21. Earl Gose, Richard Johnsonbugh and Steve Jost, (1996) Pattern Recognition and Image Analysis. Prentice Hall.
  22. Douglass R. Cutting, David R. Karger, Jan O. Pedersen and John W. Tukey, "Scatter/Gather:a clusterbasedapproach to browsing large document collections", In Proc. of the 15th annual international ACM SIGIR, June, pp. 318-329, 1992.
  23. Y. Wang, H. Song, W. Wang and M. An, "A microscopic view on community detection in complex networks, " Proceeding of the 2nd PhD Workshop on Information and Knowledge Management, New York, USA, pp. 57-64, 2008.
  24. L. A. Adamic and N. Glance, "The political blogosphere and the 2004 U.S. election:Divided they blog, "Proceedings of the 3rd International Workshop on Link Discovery, Chicago, Illinois, pp. 36-43, 2005.
  25. Valdis Krebs, "Working in the Connected World:Book Network,", IHRIM Journal, pp. 87-90, 2000.
  26. M. E. J. Newman , "Finding community structure in networks using the eigenvectors of matrices," Physical Review E, Vol. 74, No. 3, 19 pages, 2006.
  27. R. Ghosh and K. Lerman, "Structure of Heterogeneous Networks, " International Conference on Computational Science and Engineering, vol. 4, pp.98-105, 2009