Encyclopedia of Big Data Technologies

Living Edition
| Editors: Sherif Sakr, Albert Zomaya

Graph Visualization

  • Yifan Hu
  • Martin Nöllenburg
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-63962-8_324-1

Synonyms

Definition

Graph visualization is an area of mathematics and computer science, at the intersection of geometric graph theory and information visualization. It is concerned with visual representation of graphs that reveals structures and anomalies that may be present in the data and helps the user to understand and reason about the graphs.

Overview

Graph visualization is concerned with visual representations of graph or network data. Effective graph visualization reveals structures that may be present in the graphs and helps the users to understand and analyze the underlying data.

A graph consists of nodes and edges. It is a mathematical structure describing relations among a set of entities, where a node represents an entity, and an edge exists between two nodes if the two corresponding entities are related.

A graph can be described by writing down the nodes and the edges. For example, this is a social network of people and...

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

References

  1. Akoglu L, McGlohon M, Faloutsos C (2010) Oddball: Spotting anomalies in weighted graphs. In: Proceedings of the 14th Pacific-Asia conference on advances in knowledge discovery and data mining (PAKDD 2010)Google Scholar
  2. Archambault D, Purchase HC (2016) Can animation support the visualisation of dynamic graphs? Inf Sci 330(C):495–509CrossRefGoogle Scholar
  3. Brandes U, Kpf B (2002) Fast and simple horizontal coordinate assignment. In: Mutzel P, Jnger M, Leipert S (eds) Graph drawing (GD’01). LNCS, vol 2265. Springer, Berlin/Heidelberg, pp 31–44CrossRefGoogle Scholar
  4. Brandes U, Pich C (2007) Eigensolver methods for progressive multidimensional scaling of large data. In: Proceeding of the 14th international symposium graph drawing (GD’06). LNCS, vol 4372, pp 42–53Google Scholar
  5. Chimani M, Hungerlnder P, Jnger M, Mutzel P (2011) An SDP approach to multi-level crossing minimization. In: Algorithm engineering and experiments (ALENEX’11), pp 116–126Google Scholar
  6. Davis TA, Hu Y (2011) University of Florida sparse matrix collection. ACM Trans Math Softw 38:1–18MathSciNetzbMATHGoogle Scholar
  7. Di Battista G, Eades P, Tamassia R, Tollis IG (1999) Algorithms for the visualization of graphs. Prentice-Hall, Upper Saddle RiverzbMATHGoogle Scholar
  8. Duncan CA, Goodrich MT (2013) Planar orthogonal and polyline drawing algorithms, chap 7. In: Tamassia R (ed) Handbook of graph drawing and visualization. CRC Press, Hoboken, pp 223–246Google Scholar
  9. Eades P (1984) A heuristic for graph drawing. Congressus Numerantium 42:149–160MathSciNetGoogle Scholar
  10. Eades P, Wormald NC (1994) Edge crossings in drawings of bipartite graphs. Algorithmica 11:379–403MathSciNetCrossRefzbMATHGoogle Scholar
  11. Eades P, Lin X, Smyth WF (1993) A fast and effective heuristic for the feedback arc set problem. Inf Process Lett 47(6):319–323MathSciNetCrossRefzbMATHGoogle Scholar
  12. Frishman Y, Tal A (2007) Online dynamic graph drawing. In: Proceeding of eurographics/IEEE VGTC symposium on visualization (EuroVis), pp 75–82Google Scholar
  13. Fruchterman TMJ, Reingold EM (1991) Graph drawing by force directed placement. Softw Prac Exp 21:1129–1164CrossRefGoogle Scholar
  14. Gansner ER, Koutsofios E, North SC, Vo KP (1993) A technique for drawing directed graphs. IEEE Trans Softw Eng 19(3):214–230CrossRefGoogle Scholar
  15. Gansner ER, Koren Y, North SC (2004) Graph drawing by stress majorization. In: Proceeding of the 12th international symposium on graph drawing (GD’04). LNCS. vol 3383. Springer, pp 239–250Google Scholar
  16. Gansner ER, Hu Y, North SC (2013) A maxent-stress model for graph layout. IEEE Trans Vis Comput Graph 19(6):927–940CrossRefGoogle Scholar
  17. Hachul S, Jünger M (2004) Drawing large graphs with a potential field based multilevel algorithm. In: Proceeding of the 12th international symposium graph drawing (GD’04). LNCS, vol 3383. Springer, pp 285–295Google Scholar
  18. Healy P, Nikolov NS (2014) Hierarchical drawing algorithms. In: Tamassia R (ed) Handbook of graph drawing and visualization, chap 13. CRC Press, Boca Raton, pp 409–454Google Scholar
  19. Henry N, Fekete JD, McGuffin MJ (2007) Nodetrix: a hybrid visualization of social networks. IEEE Trans Vis Comput Graph 13:1302–1309CrossRefGoogle Scholar
  20. Hu Y (2005) Efficient and high quality force-directed graph drawing. Math J 10:37–71Google Scholar
  21. Ingram S, Munzner T, Olano M (2009) Glimmer: multilevel MDS on the GPU. IEEE Trans Vis Comput Graph 15:249–261CrossRefGoogle Scholar
  22. Jünger M, Mutzel P (1997) 2-layer straightline crossing minimization: performance of exact and heuristic algorithms. J Graph Algorithms Appl 1(1):1–25MathSciNetCrossRefzbMATHGoogle Scholar
  23. Kamada T, Kawai S (1989) An algorithm for drawing general undirected graphs. Inform Process Lett 31: 7–15MathSciNetCrossRefzbMATHGoogle Scholar
  24. Karp RM (1972) Reducibility among combinatorial problems. In: Miller RE, Thatcher JW, Bohlinger JD (eds) Complexity of computer computations, pp 85–103.Google Scholar
  25. Kerren A, Purchase H, Ward MO (eds) (2014) Multivariate network visualization: Dagstuhl seminar # 13201, Dagstuhl Castle, 12–17 May 2013. Revised discussions, Lecture notes in computer science, vol 8380, Springer, ChamGoogle Scholar
  26. Kruskal JB (1964) Multidimensioal scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29:1–27MathSciNetCrossRefzbMATHGoogle Scholar
  27. Kruskal JB, Seery JB (1980) Designing network diagrams. In: Proceedings of the first general conference on social graphics, U.S. Department of the Census, Washington, DC, pp 22–50, Bell laboratories technical report no. 49Google Scholar
  28. van Ham F, Wattenberg M (2008) Centrality based visualization of small world graphs. Comput Graph Forum 27(3):975–982CrossRefGoogle Scholar
  29. von Landesberger T, Kuijper A, Schreck T, Kohlhammer J, van Wijk JJ, Fekete JD, Fellner DW (2011) Visual analysis of large graphs: state-of-the-art and future research challenges. Comput Graph Forum 30(6): 1719–1749CrossRefGoogle Scholar
  30. Leskovec J, Lang K, Dasgupta A, Mahoney M (2009) Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math 6:29–123MathSciNetCrossRefzbMATHGoogle Scholar
  31. Moscovich T, Chevalier F, Henry N, Pietriga E, Fekete J (2009) Topology-aware navigation in large networks. In: CHI ’09: Proceedings of the 27th international conference on human factors in computing systems. ACM, New York, pp 2319–2328Google Scholar
  32. Ortmann M, Klimenta M, Brandes U (2016) A sparse stress model. In: Graph drawing and network visualization – 24th international symposium, GD 2016, Athens, revised selected papers, pp 18–32Google Scholar
  33. Purchase HC (1997) Which aesthetic has the greatest effect on human understanding? In: Proceeding of the 5th international symposium graph drawing (GD’97). LNCS. Springer, pp 248–261Google Scholar
  34. Quigley A (2001) Large scale relational information visualization, clustering, and abstraction. PhD thesis, Department of Computer Science and Software Engineering, University of NewcastleGoogle Scholar
  35. Rusu A (2013) Tree drawing algorithms. In: Tamassia R (ed) Handbook of graph drawing and visualization chap 5. CRC Press, Boca Raton, pp 155–192Google Scholar
  36. Sugiyama K, Tagawa S, Toda M (1981) Methods for visual understanding of hierarchical systems. IEEE Trans Syst Man Cybern SMC-11(2):109–125MathSciNetCrossRefGoogle Scholar
  37. Tamassia R (2013) Handbook of graph drawing and visualization. Chapman & Hall/CRC, Boca RatonzbMATHGoogle Scholar
  38. Tunkelang D (1999) A numerical optimization approach to general graph drawing. PhD thesis, Carnegie Mellon UniversityGoogle Scholar
  39. Vismara L (2013) Planar straight-line drawing algorithms. In: Tamassia R (ed) Handbook of graph drawing and visualization chap 6. CRC Press, Boca Raton, pp 193–222Google Scholar
  40. Walshaw C (2003) A multilevel algorithm for force-directed graph drawing. J Graph Algorithms Appl 7:253–285MathSciNetCrossRefzbMATHGoogle Scholar
  41. Wattenberg M (2006) Visual exploration of multivariate graphs. In: Proceedings of the SIGCHI conference on human factors in computing systems (CHI’06). ACM, New York, pp 811–819Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Yahoo Research, Oath IncNew YorkUSA
  2. 2.Institute of Logic and ComputationTU WienViennaAustria

Section editors and affiliations

  • Hannes Voigt
    • 1
  • George Fletcher
    • 2
  1. 1.Dresden Database Systems GroupTechnische Universität DresdenDresdenGermany
  2. 2.Department of Mathematics and Computer ScienceEindhoven University of Technology