Skip to main content
SpringerLink
Log in

Visualizing Social Networks

  • Chapter
  • First Online:
Social Network Data Analytics

Abstract

With today‘s ubiquity and popularity of social network applications, the ability to analyze and understand large networks in an efficient manner becomes critically important. However, as networks become larger and more complex, reasoning about social dynamics via simple statistics is not a feasible option. To overcome these limitations, we can rely on visual metaphors. Visualization nowadays is no longer a passive process that produces images from a set of numbers. Recent years have witnessed a convergence of social network analytics and visualization, coupled with interaction, that is changing the way analysts understand and characterize social networks. In this chapter, we discuss the main goal of visualization and how different metaphors are aimed towards elucidating different aspects of social networks, such as structure and semantics. We also describe a number of methods where analytics and visualization are interwoven towards providing a better comprehension of social structure and dynamics.

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 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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. Aleks Aris and Ben Shneiderman. Designing semantic substrates for visual network exploration. Information Visualization, 6(4):281–300, 2007.

    Article  Google Scholar 

  2. Enrico Bertini and Denis Lalanne. Surveying the complementary role of automatic data analysis and visualization in knowledge discovery. In VAKD ’09: Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery, pages 12–20, New York, NY, USA, 2009. ACM.

    Google Scholar 

  3. Philip Bonacich. Factoring and weighting approaches to status scores and clique identification. J. of Mathematical Sociology, 2(1):113–120, 1972.

    Article  Google Scholar 

  4. Ulrik Brandes. Drawing on physical analogies. pages 71–86, 2001.

    Google Scholar 

  5. Ulrik Brandes, Patrick Kenis, Jörg Raab, Volker Schneider, and Dorothea Wagner. Explorations into the visualization of policy networks. Journal of Theoretical Politics, 11(11):75–106, 1998.

    Google Scholar 

  6. Ulrik Brandes, Patrick Kenis, and Dorothea Wagner. Communicating centrality in policy network drawings. IEEE Transactions on Visualization and Computer Graphics, 9(2):241–253, 2003.

    Article  MathSciNet  Google Scholar 

  7. Ulrik Brandes and Christian Pich. An experimental study on distancebased graph drawing. pages 218–229, 2009.

    Google Scholar 

  8. Ulrik Brandes and Dorothea Wagner. Visone - analysis and visualization of social networks. In Graph Drawing Software, pages 321–340. Springer-Verlag, 2003.

    Google Scholar 

  9. Carlos D. Correa, Tarik Crnovrsanin, Kwan-Liu Ma, and Kimberly Keeton. The derivatives of centrality and their applications in visualizing social networks. Technical report, Computer Science, CSE-2009-5. University of California at Davis, 2009.

    Google Scholar 

  10. Tarik Crnovrsanin, Carlos D. Correa, and Kwan-Liu Ma. Social network discovery based on sensitivity analysis. In ASONAM ’09: Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, pages 107–112, Washington, DC, USA, 2009. IEEE Computer Society.

    Google Scholar 

  11. Patrick Doreian, Vladimir Batagelj, Anuska Ferligoj, and Mark Granovetter. Generalized Blockmodeling (Structural Analysis in the Social Sciences). Cambridge University Press, New York, NY, USA, 2004.

    Google Scholar 

  12. Tim Dwyer, Seok-Hee Hong, Dirk Koschützki, Falk Schreiber, and Kai Xu. Visual analysis of network centralities. In APVis ’06: Proceedings of the 2006 Asia-Pacific Symposium on Information Visualisation, pages 189–197, Darlinghurst, Australia, Australia, 2006. Australian Computer Society, Inc.

    Google Scholar 

  13. Linton Freeman. Visualizing social networks. Journal of Social Structure, 1(1), 2000.

    Google Scholar 

  14. Linton C Freeman. Centrality in social networks: Conceptual clarification. Social Networks, Vol. 1, No. 3(3):215 –239, 1979.

    Google Scholar 

  15. Stefan Hachul and Michael Jünger. An experimental comparison of fast algorithms for drawing general large graphs. In In Proc. Graph Drawing, pages 235–250. Springer-Verlag, 2005.

    Google Scholar 

  16. Jeffrey Heer and Danah Boyd. Vizster: Visualizing online social networks. In INFOVIS ’05: Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization, page 5, Washington, DC, USA, 2005. IEEE Computer Society.

    Google Scholar 

  17. Jeffrey Heer and Danah Boyd. Vizster: Visualizing online social networks. In Proc. IEEE Symposium on Information Visualization, page 5, 2005.

    Google Scholar 

  18. Nathalie Henry and Jean-Daniel Fekete. Matlink: enhanced matrix visualization for analyzing social networks. In INTERACT’07: Proceedings of the 11th IFIP TC 13 international conference on Human-computer interaction, pages 288–302, Berlin, Heidelberg, 2007. Springer-Verlag.

    Google Scholar 

  19. Nathalie Henry, Jean-Daniel Fekete, and Michael J. McGuffin. Nodetrix: a hybrid visualization of social networks. IEEE Transactions on Visualization and Computer Graphics, 13(6):1302–1309, 2007.

    Article  Google Scholar 

  20. Ivan Herman, Guy Melançon, and M. Scott Marshall. Graph visualization and navigation in information visualization: A survey. IEEE Transactions on Visualization and Computer Graphics, 6(1):24–43, 2000.

    Article  Google Scholar 

  21. Danny Holten. Hierarchical edge bundles: Visualization of adjacency relations in hierarchical data. IEEE Transactions on Visualization and Computer Graphics, 12(5):741–748, 2006.

    Article  Google Scholar 

  22. Yuntao Jia, Jared Hoberock, Michael Garland, and John Hart. On the visualization of social and other scale-free networks. IEEE Transactions on Visualization and Computer Graphics, 14:1285–1292, 2008.

    Google Scholar 

  23. Goh Oh Kahng, E. Oh, B. Kahng, and D. Kim. Betweenness centrality correlation in social networks. Phys. Rev. E, 67:01710–1, 2003.

    Google Scholar 

  24. Hyunmo Kang, Lise Getoor, and Lisa Singh. Visual analysis of dynamic group membership in temporal social networks. SIGKDD Explor. Newsl., 9(2):13–21, 2007.

    Article  Google Scholar 

  25. Alden S. Klovdahl. A note on images of networks. Social Networks, 3(3):197–214, 1981.

    Article  MathSciNet  Google Scholar 

  26. Yehuda Koren. On spectral graph drawing. In Proc. 9th Inter. Computing and Combinatorics Conference (COCOONÕ03), LNCS 2697, pages 496–508. Springer-Verlag, 2002.

    Google Scholar 

  27. D. Koschützki and F. Schreiber. Comparison of centralities for biological networks. In Proc German Conf Bioinformatics (GCB’04), pages 199–2006, 2004.

    Google Scholar 

  28. James Moody, Daniel Mc Farland, and Skye Bender-deMoll. Dynamic network visualization. American Journal of Sociology, 110(4):1206–1241, 2005.

    Article  Google Scholar 

  29. Chris Muelder and Kwan-Liu Ma. Rapid graph layout using space filling curves. IEEE Transactions on Visualization and Computer Graphics, 14(6):1301–1308, 2008.

    Article  Google Scholar 

  30. Galileo Mark Namata, Brian Staats, Lise Getoor, and Ben Shneiderman. A dual-view approach to interactive network visualization. In CIKM ’07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, pages 939–942, New York, NY, USA, 2007. ACM.

    Google Scholar 

  31. Andreas Noack. Modularity clustering is force-directed layout. CoRR, abs/0807.4052, 2008.

    Google Scholar 

  32. M. L. A Northway. Method for depicting social relationships obtained by sociometric testing. sociometry. 3(2), 1940.

    Google Scholar 

  33. Adam Perer and Ben Shneiderman. Balancing systematic and flexible exploration of social networks. IEEE Transactions on Visualization and Computer Graphics, 12(5):693–700, 2006.

    Article  Google Scholar 

  34. Adam Perer and Ben Shneiderman. Integrating statistics and visualization: case studies of gaining clarity during exploratory data analysis. In CHI ’08: Proc. SIGCHI conference on Human factors in computing systems, pages 265–274, 2008.

    Google Scholar 

  35. William A. Pike, John Stasko, Remco Chang and Theresa A. O’Connell. The Science of Interaction. In Information Visualization, 8(4):263–274, 2009.

    Article  Google Scholar 

  36. Zeqian Shen and Kwan-Liu Ma. Path visualization for adjacency matrices. In EuroVis, pages 83–90, 2007.

    Google Scholar 

  37. Zeqian Shen and Kwan-Liu Ma. Mobivis: A visualization system for exploring mobile data. In PacificVis, pages 175–182, 2008.

    Google Scholar 

  38. Zeqian Shen, Kwan-Liu Ma, and Tina Eliassi-Rad. 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 

  39. Lei Shi, Nan Cao, Shixia Liu, Weihong Qian, Li Tan, Guodong Wang, Jimeng Sun, and Ching-Yung Lin. Himap: Adaptive visualization of largescale online social networks. In PACIFICVIS ’09: Proceedings of the 2009 IEEE Pacific Visualization Symposium, pages 41–48, Washington, DC, USA, 2009. IEEE Computer Society.

    Google Scholar 

  40. Edward R. Tufte. The visual display of quantitative information. Graphics Press, Cheshire, CT, USA, 1986.

    Google Scholar 

  41. F. van Ham and M. Wattenberg. Centrality based visualization of small world graphs. Computer Graphics Forum, 27(3):975–982, 2008.

    Article  Google Scholar 

  42. Frank van Ham and Adam Perer. Search, Show Context, Expand on Demand: Supporting large graph exploration with degree-of-interest. IEEE Transactions on Visualization and Computer Graphics, 15(6):953–960, 2009.

    Article  Google Scholar 

  43. VAST Challenge. IEEE VAST 2008 challenge. http://www.cs.umd.edu/hcil/vastchallenge08/, 2008.

    Google Scholar 

  44. Fernanda B. Viégas, Danah Boyd, David H. Nguyen, Jeffrey Potter, and Judith Donath. Digital artifacts for remembering and storytelling: Posthistory and social network fragments. In HICSS ’04: Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS’04) - Track 4, page 40109.1, Washington, DC, USA, 2004. IEEE Computer Society.

    Google Scholar 

  45. Fernanda B. Vi?gas, Danah Boyd, David H. Nguyen, Jeffrey Potter, and Judith Donath. Digital artifacts for remembering and storytelling: Posthistory and social network fragments. Hawaii International Conference on System Sciences, 4:40109a, 2004.

    Google Scholar 

  46. Martin Wattenberg. Visual exploration of multivariate graphs. In CHI ’06: Proceedings of the SIGCHI conference on Human Factors in computing systems, pages 811–819, New York, NY, USA, 2006. ACM.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Calfornia, Davis, USA

    Carlos D. Correa & Kwan-Liu Ma

Authors
  1. Carlos D. Correa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kwan-Liu Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Carlos D. Correa .

Editor information

Editors and Affiliations

  1. Thomas J. Watson Research Center, IBM, Skyline Drive 19, Hawthorne, 10532, New York, USA

    Charu C. Aggarwal

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Correa, C.D., Ma, KL. (2011). Visualizing Social Networks. In: Aggarwal, C. (eds) Social Network Data Analytics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8462-3_11

Download citation

  • DOI: https://doi.org/10.1007/978-1-4419-8462-3_11

  • Published:

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-8461-6

  • Online ISBN: 978-1-4419-8462-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation