Social graph visualization techniques for public data

공공데이터에 적합한 다양한 소셜 그래프 비주얼라이제이션 알고리즘 제안

  • 이만재 (차세대융합기술연구원) ;
  • 온병원 (군산대학교 통계컴퓨터과학과)
  • Received : 2014.08.19
  • Accepted : 2015.01.15
  • Published : 2015.05.31

Abstract

Nowadays various public data have been serviced to the public. Through the opening of public data, the transparency and effectiveness of public policy developed by governments are increased and users can lead to the growth of industry related to public data. Since end-users of using public data are citizens, it is very important for everyone to figure out the meaning of public data using proper visualization techniques. In this work, to indicate the significance of widespread public data, we consider UN voting record as public data in which many people may be interested. In general, it has high utilization value by diplomatic and educational purposes, and is available in public. If we use proper data mining and visualization algorithms, we can get an insight regarding the voting patterns of UN members. To visualize, it is necessary to measure the voting similarity values among UN members and then a social graph is created by the similarity values. Next, using a graph layout algorithm, the social graph is rendered on the screen. If we use the existing method for visualizing the social graph, it is hard to understand the meaning of the social graph because the graph is usually dense. To improve the weak point of the existing social graph visualization, we propose Friend-Matching, Friend-Rival Matching, and Bubble Heap algorithms in this paper. We also validate that our proposed algorithms can improve the quality of visualizing social graphs displayed by the existing method. Finally, our prototype system has been released in http://datalab.kunsan.ac.kr/politiz/un/. Please, see if it is useful in the aspect of public data utilization.

Acknowledgement

Supported by : 차세대융합기술연구원

References

  1. Beauguitte, L. Looking for European Union in the word-system: A multi-graph approach. European Regional Science Association Conference. Available at http://wwwsre.wu.acat/ersa/ersaconfs/ersa10/ERSA2010finalpaper698.pdf. 2010.
  2. Brandes, U. and Wagner, D. A Bayesian paradigm for dynamic graph layout. Graph Drawing. Springer Berlin Heidelberg. 1997
  3. De Leeuw, J. Principal component analysis of binary data by iterated singular value decomposition. Computational Statistics & Data Analysis 50(1): 21-39. 2006. https://doi.org/10.1016/j.csda.2004.07.010
  4. Fruchterman, T. and Reingold, E. Graph drawing by force-directed placement. Software: Practice and Experience 21(11): 1129-1164. 1991. https://doi.org/10.1002/spe.4380211102
  5. GapMinder. Available at http://www.gapminder.org. 2014.
  6. GovTrack.us: Tracking the U.S. Congress. Available at http://www.govtrack.us
  7. Herman, I. Graph visualization and navigation in information visualization: A survey. IEEE Transactions on Visualization and Computer Graphics 6(1):24-43. 2000. https://doi.org/10.1109/2945.841119
  8. ISO 3166-1 alpha-3. Available at http://en.wikipedia.org/wiki/ISO_3166-1_alpha-3.
  9. Jakulin, A. and Buntine, W. Analyzing the US Senate in 2003: Similarities, networks, clusters and blocs. Available at http://kt.ijs.si/aleks/Politics/us_senate.pdf. 2004.
  10. Kang, U., Lee, J., Koutra, D. and Faloutsos, C. Net-Ray: Visualizing and mining billion-scale graphs. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). Taiwan. 2014.
  11. Odewahn, A. Visualizing the U.S. Senate social graph (1991-2009). Beautiful Visualization. O'Reilly. pp.123-142. 2010.
  12. OECD. Better life initiative, your life index. Available at http://www.oecdbetterlifeindex.org.
  13. Open Knowledge Foundation. Europe's energy. Available at http://energy.publicdata.eu/ee/vis.html.
  14. Open Knowledge Foundation. Where does my money go?. Available at http://wheredoesmymoneygo.org/dashboard.
  15. Voeten, E. and Merdzanovic, A. United nations general assembly voting data. Available online on 2010.
  16. 감미아, 송민. 텍스트 마이닝을 활용한 신문사에 따른 내용 및 논조 차이점 분석. 지능정보연구. 제18권 3호. pp. 53-77. 2012. https://doi.org/10.13088/JIIS.2012.18.3.053
  17. 박대민, 김기남, 강남용, 서봉원, 하효지, 온병원, 저널리즘 가치에 기초한 알고리즘을 이용한 뉴스 시각화. 한국HCI학회 논문지. 제9권 2호. 한국HCI학회. pp. 5-12. 2014.
  18. 온병원, 이인규, 이만재. Link structure based community detection 알고리즘의 제안과 소셜 네트워크 분석 및 비주얼라이제이션을 위한 사례 연구. HCI2012 학술대회 논문집. 하이원 리조트. pp. 294-297. 2012.