The Impact of Measurement Time on Subgroup Detection in Online Communities

  • Sam Zeini
  • Tilman Göhnert
  • Tobias Hecking
  • Lothar Krempel
  • H. Ulrich Hoppe
Part of the Lecture Notes in Social Networks book series (LNSN)


More and more communities use internet based services and infrastructure for communication and collaboration. All these activities leave digital traces that are of interest for research as real world data sources that can be processed automatically or semi-automatically. Since productive online communities (such as open source developer teams) tend to support the establishment of ties between actors who work on or communicate about the same or similar objects, social network analysis is a frequently used research methodology in this field. A typical application of Social Network Analysis (SNA) techniques is the detection of cohesive subgroups of actors (also called “community detection”. We were particularly interested in such methods that allow for the detection of overlapping clusters, which is the case with the Clique Percolation Method (CPM) and Link Community detection (LC). We have used these two methods to analyze data from some open source developer communities (mailing lists and log files) and have compared the results for varied time windows of measurement. The influence of the time span of data capturing/aggregation can be compared to photography: A certain minimal window size is needed to get a clear image with enough “light” (i.e. dense enough interaction data), whereas for very long time spans the image will be blurred because subgroup membership will indeed change during the time span (corresponding to a moving target). In this sense, our target parameter is “resolution” of subgroup structures. We have identified several indicators for good resolution. In general, this value will vary for different types of communities with different communication frequency and behavior. Following our findings, an explicit analysis and comparison of the influence of time window for different communities may be used to better adjust analysis techniques for the communities at hand.



The authors want to thank to Dan Suthers for the useful comments on the draft version of the chapter. We thank Umar Tarar and Ziyyad Qasim for their contributions to the implementation of the CPM-based analysis. We also thank Evelyn Fricke for the first implementation of the Link Communities analysis used in our study.


  1. 1.
    Ahn YY, Bagrow JP, Lehmann S (2010) Link communities reveal multiscale complexity in networks. Nature 466:761–764. doi:10.1038/nature09182 CrossRefGoogle Scholar
  2. 2.
    Bernard H, Killworth PD, Sailer L (1980) Informant accuracy in social network data iv: a comparison of clique-level structure in behavioral and cognitive network data. Soc Netw 2(3):191–218. doi:10.1016/0378-8733(79)90014-5
  3. 3.
    Budka M, Musial K, Juszczyszyn K (2012) Predicting the evolution of social networks: optimal time window size for increased accuracy. In: 2012 ASE/IEEE international conference on social computing (SocialCom 2012), pp 21–30Google Scholar
  4. 4.
    Burt RS (2004) Structural holes and good ideas. Am J Sociol 110(2):349–399Google Scholar
  5. 5.
    Coscia M, Giannotti F, Pedreschi D (2011) A classification for community discovery methods in complex networks. Statist Anal Data Min 4(5):512–546CrossRefMathSciNetGoogle Scholar
  6. 6.
    Diesner J, Carley K (2005) Revealing social structure from texts: meta-matrix text analysis as a novel method for network text analysis. In: Causal mapping for information systems and Te. Idea Group Publishing, Harrisburg (Chapter 4)Google Scholar
  7. 7.
    Dunbar RIM (1993) Coevolution of neocortical size, group size and language in humans. Behav Brain Sci 16:681–694. doi:10.1017/S0140525X00032325 CrossRefGoogle Scholar
  8. 8.
    Falkowski T (2009) Community analysis in dynamic social networks. Sierke Verlag, G"ottingenGoogle Scholar
  9. 9.
    Fleming L, Waguespack DM (2007) Brokerage, boundary spanning, and leadership in open innovation communities. Organ Sci 18(2):165–180. doi:10.1287/orsc.1060.0242 CrossRefGoogle Scholar
  10. 10.
    Greene D, Doyle D, Cunningham P (2010) Tracking the evolution of communities in dynamic social networks. In: Memon N, Alhajj R (eds) ASONAM, IEEE computer society, pp 176–183Google Scholar
  11. 11.
    Harrer A, Zeini S, Ziebarth S (2009) Integrated representation and visualisation of the dynamics in computer-mediated social networks. In: Memon N, Alhajj R (eds) ASONAM, IEEE computer society, pp. 261–266 (2009)Google Scholar
  12. 12.
    Palla G, Barabási AL, Vicsek T, Hungary B (2007) Quantifying social group evolution. Nature 446:664–667CrossRefGoogle Scholar
  13. 13.
    Palla G, Derényi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043):814–818.
  14. 14.
    Stegbauer C, Rausch A (2005) A moving structure: möglichkeiten der positionalen analyse von verlaufsdaten am beispiel von mailinglisten. In: Serdült U, Täube V (eds) Applications in social network analysis (Züricher Politik- und Evaluationsstudien), pp 75–98. doi:10.1007/978-3-531-90420-7_8 Google Scholar
  15. 15.
    Trier M, Bobrik A (2007) Analyzing the dynamics of community formation using brokering activities. In: Steinfield C, Pentland B, Ackerman M, Contractor N (eds) Communities and technologies 2007. Springer, London, pp 463–477. doi:10.1007/978-1-84628-905-7_23Google Scholar
  16. 16.
    Vedres B, Stark D (2010) Structural folds: generative disruption in overlapping groups. Am J Soc 115(4):1150–1190CrossRefGoogle Scholar
  17. 17.
    Wang Q, Fleury E (2010) Mining time-dependent communities. In: LAWDN—Latin-American Workshop on Dynamic Networks. INTECIN—Facultad de Ingeniería (U.B.A.)—I.T.B.A., Buenos Aires, Argentina, p 4Google Scholar
  18. 18.
    Wasserman S, Faust K (1994) Social network analysis. Methods and applications. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  19. 19.
    Zeini S, Göhnert T, Krempel L, Hoppe HU (2012) The impact of measurement time on subgroup detection in online communities. In: The 2012 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM 2012), Istanbul, TurkeyGoogle Scholar
  20. 20.
    Zeini S, Hoppe HU (2010) “Community detection” als ansatz zur identifikation von innovatoren in sozialen netzwerken. In: Meißner K, Engelien M (eds) Virtual enterprises, communities and social networks, Workshop GeNeMe 10. Gemeinschaft in Neuen Medien. TUD press, DresdenGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sam Zeini
    • 1
  • Tilman Göhnert
    • 1
  • Tobias Hecking
    • 1
  • Lothar Krempel
    • 2
  • H. Ulrich Hoppe
    • 1
  1. 1.University Duisburg-EssenDuisburg, EssenGermany
  2. 2.Max-Planck-Institute for the Study of SocietiesCologneGermany

Personalised recommendations