Skip to main content
SpringerLink
Log in

ViStruclizer: A Structural Visualizer for Multi-dimensional Social Networks

  • Conference paper
Advances in Knowledge Discovery and Data Mining (PAKDD 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7819))

Included in the following conference series:

Abstract

With the popularity of Web 2.0 sites, social networks today increasingly involve different kinds of relationships among different types of users in a single network. Such social networks are said to be multi-dimensional. Analyzing multi-dimensional networks is a challenging research task that requires intelligent visualization techniques. In this paper, we therefore propose a visual analytics tool called ViStruclizer to analyze structures embedded in a multi-dimensional social network. ViStruclizer incorporates structure analyzers that summarize social networks into both node clusters each representing a set of users, and edge clusters representing relationships between users in the node clusters. ViStruclizer supports user interactions to examine specific clusters of users and inter-cluster relationships, as well as to refine the learnt structural summary.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Airoldi, E.M., Blei, D.M., Fienberg, S.E., Xing, E.P.: Mixed membership stochastic blockmodels. Journal of Machine Learning Research 9, 1981–2014 (2008)

    MATH  Google Scholar 

  2. Batagelj, V., Mrvar, A.: Pajek - Analysis and Visualization of Large Networks. In: Mutzel, P., Jünger, M., Leipert, S. (eds.) GD 2001. LNCS, vol. 2265, pp. 477–478. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  3. Cao, N., Sun, J., Lin, Y.R., Gotz, D., Liu, S., Qu, H.: Facetatlas: Multifaceted visualization for rich text corpora. IEEE Transactions on Visualization and Computer Graphics 16(6), 1172–1181 (2010)

    Article  Google Scholar 

  4. Contractor, N.S.: The emergence of multidimensional networks. Journal of Computer-Mediated Communication 14(3), 743–747 (2009)

    Article  Google Scholar 

  5. Dai, B.T., Chua, F.C.T., Lim, E.P.: Structural analysis in multi-relational social networks. In: SDM, pp. 451–462 (2012)

    Google Scholar 

  6. Dai, B.T., Lim, E.P., Prasetyo, P.K.: Topic discovery from tweet replies. In: MLG: The Workshop on Mining and Learning with Graphs (2012)

    Google Scholar 

  7. Heer, J., Boyd, D.: Vizster: Visualizing online social networks. In: INFOVIS, p. 5 (2005)

    Google Scholar 

  8. Henry, N., Fekete, J.D.: Matrixexplorer: a dual-representation system to explore social networks. IEEE Transactions on Visualization and Computer Graphics 12(5), 677–684 (2006)

    Article  Google Scholar 

  9. McLachlan, G.J., Krishnan, T.: The EM Algorithm and Extensions. Wiley Series in probability and Statistics. Wiley (2008)

    Google Scholar 

  10. Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Physical Review E 69(6), 066133 (2004)

    Google Scholar 

  11. Shannon, R., Quigley, A.J., Nixon, P.: Graphemes: self-organizing shape-based clustered structures for network visualisations. In: CHI Extended Abstracts, pp. 4195–4200 (2010)

    Google Scholar 

  12. Shen, Z., Ma, K.L., Eliassi-Rad, T.: Visual analysis of large heterogeneous social networks by semantic and structural abstraction. IEEE Transactions on Visualization and Computer Graphics 12(6), 1427–1439 (2006)

    Article  Google Scholar 

  13. Shi, L., Cao, N., Liu, S., Qian, W., Tan, L., Wang, G., Sun, J., Lin, C.Y.: Himap: Adaptive visualization of large-scale online social networks. In: PacificVis, pp. 41–48 (2009)

    Google Scholar 

  14. Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press (1994)

    Google Scholar 

  15. White, H.C., Boorman, S.A., Breiger, R.L.: Social structure from multiple networks. i. blockmodels of roles and positions. The American Journal of Sociology 81(4), 730–780 (1976)

    Article  Google Scholar 

  16. Zinsmaier, M., Brandes, U., Deussen, O., Strobelt, H.: Interactive level-of-detail rendering of large graphs. IEEE Transactions on Visualization and Computer Graphics 18(12), 2486–2495 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Living Analytics Research Centre, Singapore Management University, 80 Stamford Road, Singapore, 178902

    Bing Tian Dai, Agus Trisnajaya Kwee & Ee-Peng Lim

Authors
  1. Bing Tian Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Agus Trisnajaya Kwee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ee-Peng Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Computing Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada

    Jian Pei

  2. Dept. of Computer Science and Information Engineering, Institute of Medical Informatics, National Cheng Kung University, Tainan, Taiwan

    Vincent S. Tseng

  3. Faculty of Engineering and Information Technology, University of Technology Sydney, Broadway, P.O. Box 123, 2007, Sydney, NSW, Australia

    Longbing Cao  & Guandong Xu  & 

  4. Asian Office of Aerospace Research and Development (AOARD), Air Force Office of Scientific Research (AFOSR), Air Force Research Laboratory USA, Osaka University, 7-23-17 Roppongi, 106-0032, Minato-ku, Tokyo, Japan

    Hiroshi Motoda

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Dai, B.T., Kwee, A.T., Lim, EP. (2013). ViStruclizer: A Structural Visualizer for Multi-dimensional Social Networks. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37456-2_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-37456-2_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37455-5

  • Online ISBN: 978-3-642-37456-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation