Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahn YY, Bagrow JP, Lehmann S (2010) Link communities reveal multiscale complexity in networks. Nature 466:761–764. doi:10.1038/nature09182
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
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 21–30
Burt RS (2004) Structural holes and good ideas. Am J Sociol 110(2):349–399
Coscia M, Giannotti F, Pedreschi D (2011) A classification for community discovery methods in complex networks. Statist Anal Data Min 4(5):512–546
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)
Dunbar RIM (1993) Coevolution of neocortical size, group size and language in humans. Behav Brain Sci 16:681–694. doi:10.1017/S0140525X00032325
Falkowski T (2009) Community analysis in dynamic social networks. Sierke Verlag, G"ottingen
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
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 176–183
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)
Palla G, Barabási AL, Vicsek T, Hungary B (2007) Quantifying social group evolution. Nature 446:664–667
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. http://dx.doi.org/10.1038/nature03607
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
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_23
Vedres B, Stark D (2010) Structural folds: generative disruption in overlapping groups. Am J Soc 115(4):1150–1190
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 4
Wasserman S, Faust K (1994) Social network analysis. Methods and applications. Cambridge University Press, Cambridge
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, Turkey
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, Dresden
Acknowledgments
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Zeini, S., Göhnert, T., Hecking, T., Krempel, L., Hoppe, H.U. (2014). The Impact of Measurement Time on Subgroup Detection in Online Communities. In: Can, F., Özyer, T., Polat, F. (eds) State of the Art Applications of Social Network Analysis. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-05912-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-05912-9_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05911-2
Online ISBN: 978-3-319-05912-9
eBook Packages: Computer ScienceComputer Science (R0)