Encyclopedia of Social Network Analysis and Mining

2018 Edition
| Editors: Reda Alhajj, Jon Rokne

Analysis and Visualization of Dynamic Networks

  • Faraz Zaidi
  • Chris Muelder
  • Arnaud Sallaberry
Reference work entry
DOI: https://doi.org/10.1007/978-1-4939-7131-2_382

Synonyms

Glossary

Clusters

A group of nodes (representing objects) that are densely connected to each other and sparsely connected to other nodes in the network. Formally, a clustering of a static graph G = (V, E) is defined by a set C of subsets of V: C = {c1, c2,…, cl} such that V = c1c2 ∪ … ∪ cl

Network or a graph

A mathematical structure to represent objects and their interactions. Objects are represented by nodes or vertices (often denoted by a set V) and interactions are represented by links or edges (often denoted by a set E). Mathematically, a graph G is defined as a tuple G(V, E). Mathematicians use the term Graph whereas scientists from other disciplines usually use the term Network to refer to the same concept. Throughout...

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References

  1. Adler RM (2007) A dynamic social network software platform for counter-terrorism decision support. In: Intelligence and security informatics, 2007 IEEE. IEEE, New Brunswick, NJ, USA, pp 47–54Google Scholar
  2. Akhmanova A, Steinmetz MO (2008) Tracking the ends: a dynamic protein network controls the fate of microtubule tips. Nat Rev Mol Cell Biol 9(4):309–322CrossRefGoogle Scholar
  3. Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512MathSciNetzbMATHCrossRefGoogle Scholar
  4. Borgatti SP, Mehra A, Brass DJ, Labianca G (2009) Network analysis in the social sciences. Science 323(5916):892–895CrossRefGoogle Scholar
  5. Brandes U, Erlebach T (eds) (2005) Network analysis: methodological foundations, lecture notes in computer science, vol 3418. Springer, New YorkGoogle Scholar
  6. Burch M, Vehlow C, Beck F, Diehl S, Weiskopf D (2011) Parallel edge splatting for scalable dynamic graph visualization. IEEE Trans Vis Comput Graph 17(12):2344–2353CrossRefGoogle Scholar
  7. Casteigts A, Flocchini P, Quattrociocchi W, Santoro N (2011) Time-varying graphs and dynamic networks. In: Proceedings of the 10th international conference on Ad-hoc, mobile, and wireless networks, Springer, ADHOC-NOW’11, Paderborn, Germany, pp 346–359Google Scholar
  8. Cazabet R, Amblard F, Hanachi C (2010) Detection of overlapping communities in dynamical social networks. In: Social computing (Social-Com), 2010 I.E. second international conference on, IEEE. Minneapolis, MN, USA, pp 309–314Google Scholar
  9. Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174MathSciNetCrossRefGoogle Scholar
  10. Freeman LC (2000) Visualizing social networks. J Soc Struct 1(1):4Google Scholar
  11. Freeman LC (2004) The development of social network analysis: a study in the sociology of science. Empirical Press, BookSurge, VancouverGoogle Scholar
  12. Frishman Y, Tal A (2008) Online dynamic graph drawing. IEEE Trans Vis Comput Graph 14(4):727–740CrossRefGoogle Scholar
  13. Gilbert F, Simonetto P, Zaidi F, Jourdan F, Bourqui R (2011) Communities and hierarchical structures in dynamic social networks: analysis and visualization. Soc Netw Anal Min 1:83–95CrossRefGoogle Scholar
  14. Holme P, Saramäki J (2012) Temporal networks. Phys Rep 519(3):97–125CrossRefGoogle Scholar
  15. Hu Y, Kobourov SG, Veeramoni S (2012) Embedding, clustering and coloring for dynamic maps. In: Proceedings of the 5th IEEE pacific visualization symposium (PacificVis 2012). Songdo, Korea, pp 33–40Google Scholar
  16. Kolar M, Song L, Ahmed A, Xing EP (2010) Estimating time-varying networks. Ann Appl Stat 4:94–123MathSciNetzbMATHCrossRefGoogle Scholar
  17. Moody J, Mcfarland D, Bender-demoll S (2005) Dynamic network visualization. Am J Sociol 110(4):1206–1241CrossRefGoogle Scholar
  18. Moreno J (1934) Who shall survive? Nervous and Mental Disease Publishing Company, Washington, DCGoogle Scholar
  19. Newcomb TM (1961) The acquaintance process. Holt, Rinehart and Winston, New YorkCrossRefGoogle Scholar
  20. Newman MEJ, Girvan M (2004) Graph clustering. Phys Rev E 69:026113CrossRefGoogle Scholar
  21. Pavlopoulos G, Wegener AL, Schneider R (2008) A survey of visualization tools for biological network analysis. BioData Min 1(1):12CrossRefGoogle Scholar
  22. Purchase H, Samra A (2008) Extremes are better: investigating mental map preservation in dynamic graphs. In: Proceedings of the 5th international conference on diagrammatic representation and inference (Diagrams 2008), vol 5223. Springer, LNCS, pp 60–73Google Scholar
  23. Robins G, Pattison P, Kalish Y, Lusher D (2007) An introduction to exponential random graph (p) models for social networks. Soc Networks 29(2):173–191CrossRefGoogle Scholar
  24. Rufiange S, McGuffin MJ (2013) Diffani: visualizing dynamic graphs with a hybrid of difference maps and animation. IEEE Trans Vis Comput Graph 19(12):2556–2565CrossRefGoogle Scholar
  25. Sallaberry A, Muelder C, Ma KL (2013) Clustering, visualizing, and navigating for large dynamic graphs. In: Proceedings of the 20th international symposium on graph drawing (GD 2012), LNCS 7704. Springer, Berlin/Heidelberg, pp 487–498Google Scholar
  26. Sampson SF (1968) A novitiate in a period of change: an experimental and case study of social relationships. PhD thesis, Cornell UniversityGoogle Scholar
  27. Schaeffer SE (2007) Graph clustering. Comput Sci Rev 1(1):27–64zbMATHCrossRefGoogle Scholar
  28. Suderman M, Hallett M (2007) Tools for visually exploring biological networks. Bioinformatics 23(20):2651–2659CrossRefGoogle Scholar
  29. Taylor IW, Linding R, Warde-Farley D, Liu Y, Pesquita C, Faria D, Bull S, Pawson T, Morris Q, Wrana JL (2009) Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol 27(2):199–204CrossRefGoogle Scholar
  30. Trier M (2008) Towards dynamic visualization for understanding evolution of digital communication networks. Inf Syst Res 19(3):335–350CrossRefGoogle Scholar
  31. Tufte ER (1990) Envisionning information. Graphics Press, CheshireGoogle Scholar
  32. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442zbMATHCrossRefGoogle Scholar

Recommended Reading

  1. Beck F, Burch M, Diehl S, Weiskopf D (2014) The state of the art in visualizing dynamic graphs STAR state of the art report, eurographics conference on visualization (EuroVis). Swansea, Wales, UKGoogle Scholar
  2. Berger-Wolf TY, Saia J (2006) A framework for analysis of dynamic social networks KDD ’06: proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM. Philadelphia, PA, USA, pp 523–528Google Scholar
  3. Holme P, Saramäki J (2012) Temporal networks. Phys Rep 519:97–125. ElsevierCrossRefGoogle Scholar
  4. Kuhn F, Oshman R (2011) Dynamic networks: models and algorithms SIGACT news. ACM 42:82–96Google Scholar
  5. Moody J, Mcfarland D, Bender-demoll S (2005) Dynamic network visualization. Am J Sociol 110:1206–1241CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media LLC, part of Springer Nature 2018

Authors and Affiliations

  • Faraz Zaidi
    • 1
    • 2
  • Chris Muelder
    • 3
  • Arnaud Sallaberry
    • 4
  1. 1.MetricAid Inc.North BayCanada
  2. 2.Karachi Institute of Economics and Technology (KIET)KarachiPakistan
  3. 3.Computer Science DepartmentUniversity of California at DavisDavisUSA
  4. 4.LIRMM – Université Paul ValéryMontpellierFrance

Section editors and affiliations

  • Tansel Ozyer
    • 1
  • Ozgur Ulusoy
    • 2
  1. 1.TOBB Economics and Technology UniversityAnkaraTurkey
  2. 2.Bilkent UniversityAnkaraTurkey