Advertisement

A Multiple-Aspects Visualization Tool for Exploring Social Networks

  • Jie Gao
  • Kazuo Misue
  • Jiro Tanaka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5618)

Abstract

Social network analysis (SNA) has been used to study the relationships between actors in social networks, revealing their features and patterns. In most cases, nodes and edges in graph theory are used to represent actors and relationships, and graph representations are used to visually analyze social networks. However, many visualization tools using network diagrams tend to depict most information about social networks by using the properties of nodes, which result in a visual burden when identifying actors or relationships according to certain properties. There is a lack of tools to support work by investigators to provide insights into multiple-aspect networks. We considered actors, relationships, and communities to be three important elements, and developed a tool called MixVis that integrates a tagcloud, network diagrams, and a list to show the elements. Our tool allows users to explore social networks from elements of interest, and acquire details through links with the three different viewpoints.

Keywords

Social network analysis visualization human interface 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Borgatti, S., Everett, M., Freeman, L.: UCINET V user’s guide. Analytic Technologies (1999)Google Scholar
  2. 2.
    Brandes, U., Wagner, D.: Visone - Analysis and Visualization of Social Networks. In: Jünger, M., Mutzel, P. (eds.) Graph Drawing Software, pp. 321–340. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    de Nooy, W., Mrvar, A., Batagelj, V.: Exploratory Social Network Analysis with Pajek. In: Structural Analysis in the Social Sciences. Cambridge University Press, New York (2005)Google Scholar
  4. 4.
    Gao, J., Misue, K., Tanaka, J.: Drawings of compound graph using free-form curves. In: Proceedings of the 70th National Convention of IPSJ, vol. 1, pp. 407–408 (2008)Google Scholar
  5. 5.
    Henry, N., Fekete, J.-D., McGuffin, M.J.: NodeTrix: A Hybrid Visualization of Social Networks. IEEE Transaction on Visualization and Computer Graphics 13(6), 1302–1309 (2007)CrossRefGoogle Scholar
  6. 6.
    Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics) 69(6) (2004)Google Scholar
  7. 7.
    Perer, A., Shneiderman, B.: Balancing Systematic and Flexible Exploration of Social Networks. IEEE Transaction on Visualization and Computer Graphics 12(5), 693–700 (2006)CrossRefGoogle Scholar
  8. 8.
    Rivadeneira, A.W., Gruen, D.M., Muller, M.J., Millen, D.R.: Getting our head in the clouds: Toward evaluation studies of tagclouds. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2007, pp. 995–998. ACM, New York (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jie Gao
    • 1
  • Kazuo Misue
    • 1
  • Jiro Tanaka
    • 1
  1. 1.Department of Computer Science, Graduate School of System and Information EngineeringUniversity of TsukubaJapan

Personalised recommendations