Skip to main content

Complex Networks, Visualization of

  • Reference work entry
Computational Complexity
  • 335 Accesses

Article Outline

Glossary

Definition of the Subject

Introduction

Attempts

Perspectives

Bibliography

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 1,500.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 1,399.99
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

Abbreviations

Glossary:

For basic notions on graphs and networks, see the articles by Wouter de Nooy: Social Network Analysis, Graph Theoretical Approaches to and by Vladimir Batagelj: Social Network Analysis, Large-Scale in the Social Networks Section. For complementary information on graph drawing in social network analysis, see the article by Linton Freeman: Social Network Visualization, Methods of.

k‑core:

A set of vertices in a graph is a k‑core if each vertex from the set has an internal (restricted to the set) degree of at least k and the set is maximal – no such vertex can be added to it.

Network:

A network consists of vertices linked by lines and additional data about vertices and/or lines. A network is large if it has at least some hundreds of vertices. Large networks can be stored in computer memory.

Partition:

A partition of a set is a family of its nonempty subsets such that each element of the set belongs to exactly one of the subsets. The subsets are also called classes or groups.

Spring embedder:

is another name for the energy minimization graph drawing method. The vertices are considered as particles with repulsive force between them, and lines as springs that attract or repel the vertices if they are too far or too close, respectively. The algorithm is a means of determining an embedding of vertices in two or three dimensional space that minimizes the ‘energy’ of the system.

Bibliography

Primary Literature

  1. Abello J, van Ham F (2004) Matrix zoom: A visual interface to semi‐external graphs. IEEE Symposium on Information Visualization, October 10–12 2004, Austin, Texas, USA, pp 183–190

    Google Scholar 

  2. Abello J, van Ham F, Krishnan N (2006) ASK‐GraphView: A large scale graph visualization system. IEEE Trans Vis Comput Graph 12(5):669–676

    Article  Google Scholar 

  3. Ahmed A, Dwyer T, Forster M, Fu X, Ho J, Hong S, Koschützki D, Murray C, Nikolov N, Taib R, Tarassov A, Xu K (2006) GEOMI: GEOmetry for maximum insight. In: Healy P, Eades P (eds) Proc 13th Int Symp Graph Drawing (GD2005). Lecture Notes in Computer Science, vol 3843. Springer, Berlin, pp 468–479

    Google Scholar 

  4. Alvarez‐Hamelin JI, DallAsta L, Barrat A, Vespignani A (2005) Large scale networks fingerprinting and visualization using the k-core decomposition. In: Advances in neural information processing systems 18, Neural Information Processing Systems, NIPS 2005, December 5–8, 2005, Vancouver, British Columbia, Canada

    Google Scholar 

  5. Batagelj V, Mrvar A (2003) Pajek – analysis and visualization of large networks. In: Jünger M, Mutzel P (eds) Graph drawing software. Springer, Berlin, pp 77–103

    Google Scholar 

  6. Batagelj V, Zaveršnik M (2002) Generalized cores. arxiv cs.DS/0202039

    Google Scholar 

  7. Batagelj V, Mrvar A, Zaveršnik M (1999) Partitioning approach to visualization of large graphs. In: Kratochvíl J (ed)Lecture notes in computer science, vol 1731. Springer, Berlin, pp 90–97

    Google Scholar 

  8. Becker RA, Eick SG, Wilks AR (1995) Visualizing network data. IEEE Trans Vis Comput Graph 1(1):16–28

    Article  Google Scholar 

  9. Boyack KW, Klavans R, Börner K (2005) Mapping the backbone of science. Scientometrics 64(3):351–374

    Google Scholar 

  10. Boyack KW, Klavans R, Paley WB (2006) Map of science. Nature 444:985

    Article  Google Scholar 

  11. Brandes U, Pich C (2007) Eigensolver methods for progressive multidimensional scaling of large data. In: Proc 14th Intl Symp Graph Drawing (GD ’06). Lecture notes in computer science, vol 4372. Springer, Berlin, pp 42–53

    Google Scholar 

  12. Brandes U, Fleischer D, Lerner J (2006) Summarizing dynamic bipolar conflict structures. IEEE Trans Vis Comput Graph (special issue on Visual Analytics) 12(6):1486–1499

    Article  Google Scholar 

  13. Dickerson M, Eppstein D, Goodrich MT, Meng J (2005) Confluent drawings: Visualizing non‐planar diagrams in a planar way. J Graph Algorithms Appl (special issue for GD’03) 9(1):31–52

    Article  MathSciNet  Google Scholar 

  14. DodgeM, Kitchin R (2001) The atlas of cyberspace. Pearson Education,Addison Wesley, New York

    Google Scholar 

  15. Doreian P, Batagelj V, Ferligoj A (2005) Generalized blockmodeling. Cambridge University Press, Cambridge

    Google Scholar 

  16. Dwyer T, Koren Y (2005) DIG-COLA: Directed graph layout through constrained energy minimization. INFOVIS 2005:9

    Google Scholar 

  17. Eades P (1984) A heuristic for graph drawing. Congressus Numerantium 42:149–160

    MathSciNet  Google Scholar 

  18. Fairchild KM, Poltrock SE, Furnas GW (1988) SemNet: Three‐dimensional representations of large knowledge bases. In: Guindon R (ed) Cognitive science and its applications for human‐computer interaction. Lawrence Erlbaum, Hillsdale, pp 201–233

    Google Scholar 

  19. FreemanLC (2000) Visualizing social networks. J Soc Struct 1(1). http://wwww.cmu.edu/joss/content/articles/volume1/Freeman/

  20. Freeman LC (2004) The development of social network analysis: A study in the sociology of science. Empirical, Vancouver

    Google Scholar 

  21. Fruchterman T, Reingold E (1991) Graph drawing by force directed placement. Softw Pract Exp 21(11):1129–1164

    Article  Google Scholar 

  22. Gajer P, Kobourov S (2001) GRIP: Graph drawing with intelligent placement. Graph Drawing 2000 LNCS, vol 1984:222–228

    Google Scholar 

  23. Gansner ER, Koren Y, North SC (2005) Topological fisheye views for visualizing large graphs. IEEE Trans Vis Comput Graph 11(4):457–468

    Article  Google Scholar 

  24. Graph Drawing. Lecture Notes in Computer Science, vol 894 (1994), 1027 (1995), 1190 (1996), 1353 (1997), 1547 (1998), 1731 (1999), 1984 (2000), 2265 (2001), 2528 (2002), 2912 (2003), 3383 (2004), 3843 (2005), 4372 (2006), 4875 (2007). Springer, Berlin

    Google Scholar 

  25. Hachul S, Jünger M (2007) Large-graph layout algorithms at work: An experimental study. JGAA 11(2):345–369

    Google Scholar 

  26. Harel D, Koren Y (2004) Graph drawing by high‐dimensional embedding. J Graph Algorithms Appl 8(2):195–214

    Article  MathSciNet  MATH  Google Scholar 

  27. Henry N, Fekete J-D (2006) MatrixExplorer: A dual‐representation system to explore social networks. IEEE Trans Vis Comput Graph 12(5):677–684

    Article  Google Scholar 

  28. Herman I, Melancon G, Marshall MS (2000) Graph visualization and navigation in information visualization: A survey. IEEE Trans Vis Comput Graph 6(1):24–43

    Article  Google Scholar 

  29. Hu YF (2005) Efficient and high quality force‐directed graph drawing. Math J 10:37–71

    Google Scholar 

  30. Kamada T, Kawai S (1988) An algorithm for drawing general undirected graphs. Inf Proc Lett 31:7–15

    Article  MathSciNet  Google Scholar 

  31. Knuth DE (1963) Computer‐drawn flowcharts. Commun ACM 6(9):555–563

    Article  Google Scholar 

  32. Koren Y (2003) On spectral graph drawing. COCOON 2003:496–508

    Google Scholar 

  33. Kruja E, Marks J, Blair A, Waters R (2001) A short note on the history of graph drawing. In: Proc Graph Drawing 2001. Lecture notesin computer science, vol 2265. Springer, Berlin, pp 272–286

    Google Scholar 

  34. Lamping J, Rao R, Pirolli P (1995) A focus+context technique based on hyperbolic geometry for visualizing large hierarchies. CHI 95:401–408

    Google Scholar 

  35. Moody J (2001) Race, school integration, and friendship segregation in America. Am J Soc 107(3):679–716

    Article  MathSciNet  Google Scholar 

  36. Moreno JL (1953) Who shall survive? Beacon, New York

    Google Scholar 

  37. Mueller C, Martin B, Lumsdaine A (2007) A comparison of vertex ordering algorithms for large graph visualization. APVIS 2007, pp 141–148

    Google Scholar 

  38. Munzner T (1997) H3: Laying out large directed graphs in 3D hyperbolic space. In: Proceedings of the 1997 IEEE Symposium on Information Visualization, 20–21 October 1997, Phoenix, AZ, pp 2–10

    Google Scholar 

  39. Noack A (2007) Energy models for graph clustering. J Graph Algorithms Appl 11(2):453–480

    Article  MathSciNet  MATH  Google Scholar 

  40. Shneiderman B (1996) The eyes have it: A task by data type taxonomy for information visualization. In: IEEE Conference on Visual Languages (VL’96). IEEE CS Press, Boulder

    Google Scholar 

  41. Shneiderman B, Aris A (2006) Network visualization by semantic substrates. IEEE Trans Vis Comput Graph 12(5):733–740

    Article  Google Scholar 

  42. Sugiyama K, Tagawa S, Toda M (1981) Methods for visual understanding of hierarchical systems. IEEE Trans Syst, Man, Cybern 11(2):109–125

    Article  MathSciNet  Google Scholar 

  43. Tutte WT (1963) How to draw a graph. Proc London Math Soc s3-13(1):743–767

    Article  MathSciNet  Google Scholar 

  44. Walshaw C (2003) A multilevel algorithm for force‐directed graph‐drawing. J Graph Algorithms Appl 7(3):253–285

    Article  MathSciNet  MATH  Google Scholar 

  45. Wetherell C, Shannon A (1979) Tidy drawing of trees. IEEE Trans Softw Engin 5:514–520

    Article  MATH  Google Scholar 

  46. White DR, Jorion P (1992) Representing and computing kinship: A new approach. Curr Anthr 33(4):454–463

    Article  Google Scholar 

  47. Wills GJ (1999) NicheWorks‐interactive visualization of very large graphs. J Comput Graph Stat 8(2):190–212

    MathSciNet  Google Scholar 

Web Resources

  1. 3D Macromolecule analysis and Kinemage home page: http://kinemage.biochem.duke.edu/. Accessed March 2008

  2. Aguidel: http://www.aguidel.com/en/. Accessed March 2008

  3. Batagelj V, Mrvar A (1996) Pajek – program for analysis and visualization of large network: http://pajek.imfm.si. Accessed March 2008. Data sets: http://vlado.fmf.uni-lj.si/pub/networks/data/. Accessed March 2008

  4. Brookhaven Protein Data Bank: http://www.rcsb.org/pdb/. Accessed March 2008

  5. Caida: http://www.caida.org/home/. Accessed March 2008. Walrus gallery: http://www.caida.org/tools/visualization/walrus/gallery1/. Accessed March 2008

  6. Cheswick B: Internet mapping project – map gallery: http://www.cheswick.com/ches/map/gallery/. Accessed March 2008

  7. Complex Networks Collaboratory: http://cxnets.googlepages.com/. Accessed March 2008

  8. Cruz I, Tamassia R (1994) Tutorial on graph drawing. http://graphdrawing.org/literature/gd-constraints.pdf. Accessed March 2008

  9. Davis T: University of Florida Sparse Matrix Collection: http://www.cise.ufl.edu/research/sparse/matrices. Accessed March 2008

  10. Di Battista G, Eades P, Tamassia R, Tollis IG (1994) Algorithms for drawing graphs: An annotated bibliography. Comput Geom: Theory Appl 4:235–282. http://graphdrawing.org/literature/gdbiblio.pdf. Accessed March 2008

  11. Dodge M: Cyber‐Geography Research: http://personalpages.manchester.ac.uk/staff/m.dodge/cybergeography/. Accessed March 2008

  12. Edinburgh Associative Thesaurus (EAT): http://www.eat.rl.ac.uk/. Accessed March 2008

  13. FamilySearch: http://www.familysearch.org/. Accessed March 2008

  14. FASresearch, Vienna, Austria: http://www.fas.at/. Accessed March 2008

  15. Follow the Oil Money: http://oilmoney.priceofoil.org/. Accessed March 2008

  16. GDToolkit – Graph Drawing Toolkit: http://www.dia.uniroma3.it/~gdt/gdt4/index.php. Accessed March 2008

  17. GEOMI (Geometry for Maximum Insight): http://www.cs.usyd.edu.au/~visual/valacon/geomi/. Accessed March 2008

  18. Google Maps: http://maps.google.com/. Accessed March 2008

  19. Graphael: http://graphael.cs.arizona.edu/. Accessed March 2008

  20. Graphdrawing home page: http://graphdrawing.org/. Accessed March 2008

  21. GraphML File Format: http://graphml.graphdrawing.org/. Accessed March 2008

  22. Graphviz: http://graphviz.org/. Accessed March 2008

  23. GRIP: http://www.cs.arizona.edu/~kobourov/GRIP/. Accessed March 2008

  24. Grokker – Enterprise Search Management and Content Integration: http://www.grokker.com/. Accessed March 2008

  25. Henry N, Fekete J-D, Mcguffin M (2007) NodeTrix: Hybrid representation for analyzing social networks: https://hal.inria.fr/inria-00144496. Accessed March 2008

  26. Herr BW, Holloway T, Börner K (2007) Emergent mosaic of wikipedian activity: http://www.scimaps.org/dev/big_thumb.php?map_id=158. Accessed March 2008

  27. Hu YF: Gallery of Large Graphs: http://www.research.att.com/~yifanhu/GALLERY/GRAPHS/index1.html. Accessed March 2008

  28. iCKN: TeCFlow – a temporal communication flow visualizer for social network analysis: http://www.ickn.org/. Accessed March 2008

  29. ILOG Diagrams: http://www.ilog.com/. Accessed March 2008

  30. Infovis – 1100+ examples of information visualization: http://www.infovis.info/index.php?cmd=search&words=graph&mode=normal. Accessed March 2008

  31. INSNA – International Network for Social Network Analysis: http://www.insna.org/. Accessed March 2008

  32. Journal of Graph Algorithms and Applications: http://jgaa.info/. Accessed March 2008

  33. KartOO visual meta search engine: http://www.kartoo.com/. Accessed March 2008

  34. LaNet-vi – Large Network visualization tool: http://xavier.informatics.indiana.edu/lanet-vi/. Accessed March 2008

  35. MDL Chime: http://www.mdli.com/. Accessed March 2008

  36. Moody J (2007) The network structure of sociological production II: http://www.soc.duke.edu/~jmoody77/presentations/soc_Struc_II.ppt. Accessed March 2008

  37. Mueller C: Matrix visualizations: http://www.osl.iu.edu/~chemuell/data/ordering/sparse.html. Accessed March 2008

  38. OLIVE, On-line library of information visualization environments: http://otal.umd.edu/Olive/. Accessed March 2008

  39. Pad++: Zoomable user interfaces: Portal filtering and ‘magic lenses’: http://www.cs.umd.edu/projects/hcil/pad++/tour/lenses.html. Accessed March 2008

  40. RasMol Home Page: http://www.umass.edu/microbio/rasmol/index2.htm. Accessed March 2008

  41. Sonia – Social Network Image Animator: http://www.stanford.edu/group/sonia/. Accessed March 2008

  42. SPSS nViZn: http://www.spss.com/research/wilkinson/nViZn/nvizn.html. Accessed March 2008

  43. SVGanim: http://vlado.fmf.uni-lj.si/pub/networks/pajek/SVGanim. Accessed March 2008

  44. Tom Sawyer Software: http://www.tomsawyer.com/home/index.php. Accessed March 2008

  45. TouchGraph: http://www.touchgraph.com/. Accessed March 2008

  46. Tulip: http://www.labri.fr/perso/auber/projects/tulip/. Accessed March 2008

  47. Viégas FB, Wattenberg M (2007) Many Eyes: http://services.alphaworks.ibm.com/manyeyes/page/Network_Diagram.html. Accessed March 2008

  48. Visual complexity: http://www.visualcomplexity.com/vc/. Accessed March 2008

  49. yWorks/yFiles: http://www.yworks.com/en/products_yfiles_about.htm. Accessed March 2008

Books and Reviews

  1. Bertin J (1967) Sémiologie graphique. Les diagrammes, les réseaux, les cartes. Mouton/Gauthier‐Villars, Paris/La Haye

    Google Scholar 

  2. Brandes U, Erlebach T (eds) (2005) Network analysis: Methodological foundations. LNCS. Springer, Berlin

    Google Scholar 

  3. Carrington PJ, Scott J, Wasserman S (eds) (2005) Models and methods in social network analysis. Cambridge University Press, Cambridge

    Google Scholar 

  4. de Nooy W, Mrvar A, Batagelj V (2005) Exploratory social network analysis with Pajek. Cambridge University Press, Cambridge

    Book  Google Scholar 

  5. di Battista G, Eades P, Tamassia R, Tollis IG (1999) Graph drawing: Algorithms for the visualization of graphs. Prentice Hall,Englewood Cliffs

    MATH  Google Scholar 

  6. Jünger M, Mutzel P (eds) (2003) Graph drawing software. Springer, Berlin

    Google Scholar 

  7. Kaufmann M, Wagner D (2001) Drawing graphs, methods and models. Springer, Berlin

    Book  MATH  Google Scholar 

  8. Tufte ER (1983) The visual display of quantitative information. Graphics, Cheshire

    Google Scholar 

  9. Wasserman S, Faust K (1994) Social network analysis: Methods and applications. Cambridge University Press, Cambridge

    Book  Google Scholar 

  10. Wilkinson L (2000) The grammar of graphics. Statistics and Computing. Springer, Berlin

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag

About this entry

Cite this entry

Batagelj, V. (2012). Complex Networks, Visualization of. In: Meyers, R. (eds) Computational Complexity. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1800-9_38

Download citation

Publish with us

Policies and ethics