Social Network Big Data 분석 기법과 응용

  • Published : 2014.10.31

Abstract

최근 정보통신 기술의 발전과 더불어 급성장 중인 소셜 네트워크는 개인 혹은 집단간의 실제 사회적 관계를 네트워크 구조로 반영하고 있다. 소셜 네트워크를의 구조를 보다 정확하게 이해하고 소셜 네트워크 내에서 정보가 전파되는 패턴을 파악하기 위해 소셜 네트워크를 수학적으로 모델링하고, 이를 응용하여 소셜 네트워크 빅 데이터를 분석하는 다양한 연구가 이루어지고 있다. 본고에서는 소셜 네트워크의 구조 분석과 정보 확산 패턴 파악에 관한 주요 연구 사례들을 소개하고, 특히 소셜 빅 데이터 분석과 관련된 연구 주제 및 응용 사례들을 살펴보고자 한다.

Keywords

References

  1. N. Alon and J. H. Spencer, The Probabilistic Method, Wiley, 1992, xiii+254 pp.
  2. A. Arenas, A. Diaz-Guilera, and C. J. Perez-Vicente, Synchronization Reveals Topological Scales in Complex Networks, Phys. Rev. Lett. 96 114102, 2006. https://doi.org/10.1103/PhysRevLett.96.114102
  3. L. Backstrom and J. Leskovec, Supervised Random Walks: Predicting and Recommending Links in Social Networks, In proc. of the 4th ACM WSDM conference, 2011.
  4. A. Barabasi, Linked: The New Science of Networks, Perseus Publishing, April 2002.
  5. S. Brin and L. Page, The Anatomy of a Largescale Hypertextual Web Search Engine, Computer Networks and ISDN Systems, 30(1-7):107-117, 1998. https://doi.org/10.1016/S0169-7552(98)00110-X
  6. G. Salton and M. J. McGill. Introduction to Modern Information Retrieval, McGraw-Hill, 1983.
  7. W. Chen, C. Wang, and Y. Wang, Scalable Influence Maximization for prevalent viral marketing in large scale social networks, In proc. of the 16th ACM SIGKDD, 2010.
  8. Y. Sun, J. Han, J. Gao, and Y. Yu, iTopicModel: Information Network-Integrated Topic Modeling, In proc. of the 10th IEEE ICDM, 2009.
  9. V. D. Blondel, J. Guillaume, R. Lambiotte, E. Lefebvre, Fast unfolding of communities in large networks, J. Stat. Mech 2008(10)., P10008, 2008 https://doi.org/10.1088/1742-5468/2008/10/P10008
  10. Bloter.net, [그래프] 주요 커뮤니티의 모바일 체류시간, http://www.bloter.net/archives/173396
  11. J. C. Miller, Percolation and Epidemics in Random Clustered Networks, Phys. Rev. E 80, 020901, 2009. https://doi.org/10.1103/PhysRevE.80.020901
  12. J. Goldenberg, B. Libai, and E. Muller, Talk of the network: A complex systems look at the underlying process of word-of-mouth, Marketing Letters. Vol. 12, No. 3, pp.211-223, 2001. https://doi.org/10.1023/A:1011122126881
  13. M. Granovetter, Threshold Models of Collective Behavior, American journal of sociology. Vol. 83, No. 6, pp.1420-1443, 1978. https://doi.org/10.1086/226707
  14. T. Yang, R. Jin, Y. Chi, and S. Zhu, Combining Link and Content for Community Detection: a Discriminative Approach, In proc. of the 15th SIGKDD, 2009.
  15. D. E. Whitney, Dynamic Theory of Cascades on Finite Clustered Random Networks with a Threshold Rule, Phys. Rev. E 82, 066110, 2010. https://doi.org/10.1103/PhysRevE.82.066110
  16. 정하웅, 강병남, 복잡계 네트워크에 대한 최근 연구 동향, 물리학과 첨단기술, 2007년 10월호.
  17. F. Kuhn, K. Panagiotou, J. Spencer, and A. Steger, Synchrony and Asynchrony in Neural Networks, In proc. of the 21st SODA, 2010.
  18. B. Karrer and M. E. J. Newman, Random Graphs Containing Arbitrary Distributions of Subgraphs, Phys. Rev. E 82, 066118, 2010. https://doi.org/10.1103/PhysRevE.82.066118
  19. L. Katz. A New Status Index Derived from Sociometric Analysis, Psychometrika, 18(1):39-43, 1953. https://doi.org/10.1007/BF02289026
  20. D. Kempe, J. Kleinberg, and É. Tardos, Maximizing the Spread of Influence Through a Social Network. In proc. of the 9th ACM SIGKDD, 2003.
  21. J. Kleinberg, Navigation in a Small World, Nature 406:845, 2000. https://doi.org/10.1038/35022643
  22. J. Leskovec and J. Kleinberg and C. Faloutsos, Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations, In proc. of the 11th ACM SIGKDD, 2005.
  23. J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. Van-Briesen, and N. S. Glance, Cost-effective Outbreak Detection in Networks, In proc. of the 13th ACM SIGKDD, 2007.
  24. M. E. J. Newman and M. Girvan, Finding and Evaluating Community Structure in Networks, Phys. Rev. E 69, 026113, 2004. https://doi.org/10.1103/PhysRevE.69.026113
  25. S. Zhu, K. Yu, Y. Chi, and Y. Gong, Combining Content and Link for Classification Using Matrix Factorization, In proc. of the 30th SIGIR, 2007.
  26. B. Shaw, B. Huang and T. Jebara, Learning a Distance Metric from a Network, In proc. of the 25th NIPS, 2011.
  27. S. Lim, S. Ryu, S. Kwon, K. Jung and J. Lee, LinkSCAN∗: Overlapping Community Detection Using the Link-Space Transformation, ICDE 2014, pp292-303, 2014
  28. G. Palla, I. Derenyi, I. Farkas, and T. Vicsek, Uncovering the overlapping community structure of complex networks in nature and society, Nature, vol. 435, pp. 814-818, 2005. https://doi.org/10.1038/nature03607