Computational Statistics

, Volume 32, Issue 2, pp 535–557 | Cite as

An interactive graphical method for community detection in network data

  • Andee Kaplan
  • Heike Hofmann
  • Daniel Nordman
Original Paper


The detection of community structures within network data is a type of graph analysis with increasing interest across a broad range of disciplines. In a network, communities represent clusters of nodes that exhibit strong intra-connections or relationships among nodes in the cluster. Current methodology for community detection often involves an algorithmic approach, and commonly partitions a graph into node clusters in an iterative manner before some stopping criterion is met. Other statistical approaches for community detection often require model choices and prior selection in Bayesian analyses, which are difficult without some amount of data inspection and pre-processing. Because communities are often fuzzily-defined human concepts, an alternative approach is to leverage human vision to identify communities. The work presents a tool for community detection in form of a web application, called gravicom, which facilitates the detection of community structures through visualization and direct user interaction. In the process of detecting communities, the gravicom application can serve as a standalone tool or as a step to potentially initialize (and/or post-process) another community detection algorithm. In this paper we discuss the design of gravicom and demonstrate its use for community detection with several network data sets. An “Appendix” describes details in the technical formulation of this web application built on the R package Shiny and the JavaScript library D3.


Graph layout Interactive graphics Web application Human perception 


  1. Airoldi EM, Blei DM, Fienberg SE, Xing EP (2009) Mixed membership stochastic blockmodels. In: Koller D, Schuurmans D, Bengio Y, Bottou L (eds) Advances in neural information processing systems 21. Curran Associates Inc, Red Hook, pp 33–40Google Scholar
  2. Amini AA, Chen A, Bickel PJ, Levina E (2013) Pseudo-likelihood methods for community detection in large sparse networks. Ann Stat 41(4):2097–2122MathSciNetCrossRefzbMATHGoogle Scholar
  3. Ball B, Karrer B, Newman MEJ (2011) Efficient and principled method for detecting communities in networks. Phys Rev E 84(036):103Google Scholar
  4. Bastian M, Heymann S, Jacomy M (2009) Gephi: an open source software for exploring and manipulating networks.
  5. Bickel P, Choi D, Chang X, Zhang H (2013) Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels. Ann Stat 41(4):1922–1943MathSciNetCrossRefzbMATHGoogle Scholar
  6. Bostock M, Ogievetsky V, Heer J (2011) D3: data-driven documents. IEEE Trans Vis Computer Gr 17(12):2301–2309CrossRefGoogle Scholar
  7. Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70(066):111Google Scholar
  8. Couture-Beil A (2013) rjson: JSON for R., R package version 0.2.12
  9. Csardi G, Nepusz T (2006) The igraph software package for complex network research. InterJ Complex Syst :1695,
  10. Cukier J (2012) Events in the game of thrones.
  11. Duch J, Arenas A (2005) Community detection in complex networks using extremal optimization. Phys Rev E 72(027):104Google Scholar
  12. Dunne C, Shneiderman B (2013) Motif simplification: improving network visualization readability with fan, connector, and clique glyphs. In: Proceedings of the SIGCHI conference on human factors in computing systems, ACM, New York, NY, USA, CHI ’13, pp 3247–3256Google Scholar
  13. Dwyer T, Lee B, Fisher D, Quinn KI, Isenberg P, Robertson G, North C (2009) A comparison of user-generated and automatic graph layouts. IEEE Trans Vis Computer Gr 15(6):961–968CrossRefGoogle Scholar
  14. Fortunato S (2010) Community detection in graphs. Phys Rep 486(35):75–174MathSciNetCrossRefGoogle Scholar
  15. Gandrud C (2014) d3Network: tools for creating D3 JavaScript network, tree, dendrogram, and Sankey graphs from R., R package version 0.5.1
  16. Gansner ER, North SC (2000) An open graph visualization system and its applications to software engineering. Softw Pract Exp 30(11):1203–1233CrossRefzbMATHGoogle Scholar
  17. Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826MathSciNetCrossRefzbMATHGoogle Scholar
  18. Greene D, Doyle D, Cunningham P (2010) Tracking the evolution of communities in dynamic social networks. In: Advances in Social Networks Analysis and Mining (ASONAM), international conference on 2010, pp 176–183Google Scholar
  19. Guo J, Wilson AG, Nordman DJ (2013) Bayesian nonparametric models for community detection. Technometrics 55(4):390–402MathSciNetCrossRefGoogle Scholar
  20. Hansen D, Shneiderman B, Smith MA (2010) Analyzing social media networks with NodeXL: Insights from a connected world. Morgan Kaufmann, BurlingtonGoogle Scholar
  21. Hofman JM, Wiggins CH (2008) Bayesian approach to network modularity. Phys Rev Lett 100(258):701Google Scholar
  22. Holland PW, Leinhardt S (1981) An exponential family of probability distributions for directed graphs. J AmStat Assoc 76(373):33–50MathSciNetCrossRefzbMATHGoogle Scholar
  23. Karrer B, Newman MEJ (2011) Stochastic blockmodels and community structure in networks. Phys Rev E 83(016):107MathSciNetGoogle Scholar
  24. Kawadia V, Sreenivasan S (2012) Sequential detection of temporal communities by estrangement confinement. Sci Rep 2,
  25. Kemp C, Tenenbaum JB, Griffiths TL, Yamada T, Ueda N (2006) Learning systems of concepts with an infinite relational model. In: Proceedings of the 21st national conference on artificial intelligence - Vol 1, AAAI Press, AAAI’06, pp 381–388Google Scholar
  26. Kling F (2014) Jsnetworkx: A javascript port of the networkx graph library.
  27. Krebs V (2004a) Books about US politics.
  28. Krebs V (2004b) Divided we stand... still.
  29. Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E 78(046):110Google Scholar
  30. Leskovec J, Lang KJ, Dasgupta A, Mahoney MW (2008) Statistical properties of community structure in large social and information networks. In: Proceedings of the 17th International Conference on World Wide Web, ACM, New York, NY, USA, WWW ’08, pp 695–704Google Scholar
  31. Leskovec J, Lang KJ, Mahoney M (2010) Empirical comparison of algorithms for network community detection. In: Proceedings of the 19th International Conference on World Wide Web, ACM, New York, NY, USA, WWW ’10, pp 631–640Google Scholar
  32. Martin GR (1996) A game of thrones. Bantam Books, New YorkGoogle Scholar
  33. Martin GR (1999) A clash of kings. Bantam Books, New YorkGoogle Scholar
  34. Martin GR (2000) A storm of swords. Bantam Books, New YorkGoogle Scholar
  35. Martin GR (2005) A feast for crows. Bantam Books, New YorkGoogle Scholar
  36. Martin GR (2011) A dance with dragons. Bantam Books, New YorkGoogle Scholar
  37. McGrath C, Blythe J, Krackhardt D (1996) Seeing groups in graph layouts. Connections 19(2):22–29Google Scholar
  38. Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45(2):167–256MathSciNetCrossRefzbMATHGoogle Scholar
  39. Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(026):113Google Scholar
  40. Nowicki K, Snijders TAB (2001) Estimation and prediction for stochastic blockstructures. J Am Stat Assoc 96(455):1077–1087MathSciNetCrossRefzbMATHGoogle Scholar
  41. R Core Team (2014) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria,
  42. Rosvall M, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci 105(4):1118–1123CrossRefGoogle Scholar
  43. RStudio Inc (2013) shiny: Web application framework for R., R package version 0.4.0
  44. Wasserman S, Faust K (1994) Social network analysis: methods and applications, vol 8. Cambridge University Press, New YorkCrossRefzbMATHGoogle Scholar
  45. Xie J, Chen M, Szymanski BK (2013) Labelrankt: Incremental community detection in dynamic networks via label propagation. In: Proceedings of the workshop on dynamic networks management and mining, ACM, New York, NY, USA, DyNetMM ’13, pp 25–32Google Scholar
  46. Zachary WW (1977) An information flow model for conflict and fission in small groups. J Anthropol Res 33(4):452–473CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of StatisticsIowa State UniversityAmesUSA

Personalised recommendations