A Visualization Framework of Information Flows on a Very Large Social Network

초대형 사회망에서의 정보 흐름의 시각화 프레임워크

  • 김신규 (서울대학교 컴퓨터공학부) ;
  • 염헌영 (서울대학교 컴퓨터공학부)
  • Published : 2009.06.30

Abstract

Recently, the information visualization research community has given significant attention to graph visualization, especially visualization of social networks. However, visualization of information flows in a very large social network has not been studied in depth. However, information flows are tightly related to the structure of social networks and it shows dynamic behavior of interactions between members of social networks. Thus, we can get much useful information about social networks from information flows. In this paper, we present our research result that enables users to navigate a very large social network in Google Maps' method and to take a look at information flows on the network. To this end, we devise three techniques; (i) mapping a very large social network to a 2-dimensional graph layout, (ii) exploring the graph to all directions with zooming it in/out, and (iii) building an efficient query processing framework. With these methods, we can visualize very large social networks and information flows in a limited display area with a limited computing resources.

최근 정보의 시각화를 연구하는 쪽에서는 그래프의 시각화에 많은 관심을 갖고 있는데, 그 중 사회망 (social network)의 시각화에 특히 집중하고 있다. 하지만 아직까지 사회망 내에서의 정보의 흐름을 시각화하는 방법에 대해서는 깊이 있는 연구가 진행되지 않고 있었다. 정보의 흐름은 사회망의 구조와 밀접하게 연관되어 있고, 또한 실제적인 상호관계의 동적인 구성을 보여주기 때문에 사회망의 구조적 특징보다 더 유용한 정보를 담고 있다. 따라서 정보의 흐름을 시각화하는 것은 매우 중요하다. 본 논문에서는 초대형 사회망을 마치 온라인 지도서비스를 이용하듯이 탐색하고, 사회망 내에서의 정보의 흐름을 관찰할 수 있는 방법에 관하여 제안한다. 이를 위하여 (i) 초대형 사회망을 2차원 그래프에 맵핑하는 방법과, (ii) 줌-인, 줌-아웃 기능 등을 활용하여 그래프를 탐색하는 방법, 그리고 (iii) 효율적인 질의 처리 프레임웍을 구축하는 방법을 고안하였다. 이 방법들을 이용하여 초대형 사회망을 제한적인 공간과 한정된 자원을 이용하여 효과적으로 표현할 수 있고, 이에 기반을 두어 사회망에서의 정보의 흐름을 시각화할 수 있게 된다.

Keywords

References

  1. Car Dealer Social Network, http://autodealers.ning.com.
  2. CarSpace, http://www.carspace.com/.
  3. Facebook, http://www.facebook.com/.
  4. Google Maps, http://maps.google.com/.
  5. GraphML, http://graphml.graphdrawing.org.
  6. Jung: Java universal network/graph framework. http://jung.sf.net/.
  7. Lucene, http://lucene.apache.org/.
  8. MapQuest Maps, http://www.mapquest.com/.
  9. MySpace, http://www.myspace.com/.
  10. Ucinet: Social network analysis software. http://analytictech.com/.
  11. A. Abou-Rjeili and G. Karypis. 'Multilevel algorithms for partitioning power-law graphs', Proceedings of IPDPS06, 2006.
  12. L. Adamic, O. Buyukkokten, and E. Adar. 'A social network caught in the web', First Monday, 2003.
  13. E. Adar. Guess: The graph exploration system. http://www.hpl.hp.col/research/idl/projects/graphs.
  14. Y.-Y. Ahn, S. Han, H. Kwak, S. Moon, and H. Jeong. 'Analysis of topological characteristics of huge online social networking services', Proceedings of the 16th international conference on World Wide Web, pages 835–844, 2007
  15. D. Archambault, T. Munzner, and D. Auber. 'Grouseflocks: Steerable exploration of graph hierarchy space', IEEE Transactions on Visualization and Computer Graphics, 14(4):900–913, 2008.
  16. D. Chakrabarti, Y. Zhan, and C. Faloutsos. 'R-mat: A recursive model for graph mining'. In SDM, 2004.
  17. D. Fisher and P. Dourish. 'Social and temporal structures in everyday collaboration', Proceedings of the SIGCHI conference on Human factors in computing systems, pages 551–558, 2004
  18. L. C. Freeman. 'Visualizing social networks', Journal of Social Structure, 2000.
  19. J. Heer and D. Boyd. 'Vizster: Visualizing online social networks', Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization, page 5, 2005
  20. J. Heer, S. K. Card, and J. A. Landay. 'prefuse: a toolkit for interactive information visualization', Proceedings of the SIGCHI conference on Human factors in computing systems, pages 421–430, 2005
  21. S. Hong, B. Moon, and S. Lee. 'Efficient execution of range top-k queries in aggregate r-trees', IEICE-Trans. Inf. Syst., E88-D(11):2544–2554, 2005.
  22. H. Ishii, S. Ren, and P. Frei. 'Pinwheels: visualizing information flow in an architectural space', CHI ’01 extended abstracts on Human factors in computing systems, pages 111–112, 2001
  23. S. M. LaValle. 'Planning Algorithms', Cambridge University Press, 2006.
  24. I. Lazaridis and S. Mehrotra. 'Progressive approximate aggregate queries with a multi-resolution tree structure', SIGMOD Rec., 30(2):401–412, 2001.
  25. L. S. Marion, E. Garfield, L. L. Hargens, L. A. Lievrouw, H. D. White, and C. S. Wilson. 'Social network analysis and citation network analysis: Complementary approaches to the study of scientific communication', Proceedings of the American Society for Information Science and Technology, pages 486–487, 2005.
  26. M. Newman. 'Co-authorship networks and patterns of scientific collaboration', Proceedings Of The National Academy Of Sciences Of The United States Of America, pages 5200–5205, 2004.
  27. L. Page, S. Brin, R. Motwani, and T. Winograd. 'The pagerank citation ranking: Bringing order to the web', 1999.
  28. D. Papadias, P. Kalnis, J. Zhang, and Y. Tao. 'Efficient olap operations in spatial data warehouses', Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases, pages 443–459, 2001
  29. Travers, Jeffrey, and S. Milgram. 'An experimental study of the small world problem', Sociometry, 32(4):425–443, 1969.
  30. F. van Ham and N. Krishnan. 'Ask-graphview: A large scale graph visualization system', IEEE Transactions on Visualization and Computer Graphics, 12(5):669–676, 2006
  31. F. B. Vi´egas and J. Donath. 'Social network visualization: Can we go beyond the graph', Workshop on Social Networks, CSCW, 4:6–10, 2004.
  32. F. Wu, B. A. Huberman, L. A. Adamic, and J. R. Tyler. 'Information flow in social groups. Physica A Statistical Mechanics and its Applications',337:327–335, June 2004.
  33. IMC 2007 Data Sets, http://socialnetworks.mpisws.org/data-imc2007.html.
  34. Flickr, http://www.flickr.com
  35. Orkut, http://www.orkut.com