Analyses of a Virtual World

  • Yurij Holovatch
  • Olesya MryglodEmail author
  • Michael Szell
  • Stefan Thurner
Part of the Understanding Complex Systems book series (UCS)


We present an overview of a series of results obtained from the analysis of human behavior in a virtual environment. We focus on the massive multiplayer online game (MMOG) Pardus which has a worldwide participant base of more than 400,000 registered players. We provide evidence for striking statistical similarities between social structures and human-action dynamics in real and virtual worlds. In this sense MMOGs provide an extraordinary way for accurate and falsifiable studies of social phenomena. We further discuss possibilities to apply methods and concepts developed in the course of these studies to analyse oral and written narratives.


Virtual World Cluster Coefficient Node Degree Preferential Attachment Multiplex Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We want to express our thanks to the Editors of the book, Ralph Kenna, Máirín MacCarron, and Pádraig MacCarron, for the invitation to write this chapter and for useful suggestions and to Anita Wanjek for helpful comments on the manuscript. This work was supported in part by the 7th FP, IRSES project No. 612707 Dynamics of and in Complex Systems (DIONICOS) and by the COST Action TD1210 Analyzing the dynamics of information and knowledge landscapes (KNOWSCAPE). ST acknowledges support by the EU FP7 project LASAGNE no. 318132.


  1. Barabási, A. (2005). The origin of bursts and heavy tails in human dynamics. Nature, 435, 207.ADSCrossRefGoogle Scholar
  2. Barabási, A., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509.ADSMathSciNetCrossRefzbMATHGoogle Scholar
  3. Bohorquez, J. C., Gourley, S., Dixon, A. R., Spagat, M., & Johnson, N. F. (2009). Common ecology quantifies human insurgency. Nature, 462, 911.ADSCrossRefGoogle Scholar
  4. Castronova, E. (2005). Synthetic worlds. The business and culture of online games (332 pp.). Chicago: The University of Chicago Press.Google Scholar
  5. Clauset, A., & Gleditsch, K. S. (2012). The developmental dynamics of terrorist organizations. PLoS ONE, 7(11), e48633.ADSCrossRefGoogle Scholar
  6. Corominas-Murtra, B., Fuchs, B., & Thurner, S. (2014). Detection of the elite structure in a virtual multiplex social system by means of a generalised K-core. PLoS ONE, 9(12), e112606.ADSCrossRefGoogle Scholar
  7. Dunbar, R. (1993). Coevolution of neocortical size, group size and language in humans. Behavioral and Brain Sciences, 16(4), 681.CrossRefGoogle Scholar
  8. Fuchs, B., Sornette, D., & Thurner, S. (2014). Fractal multi-level organisation of human groups in a virtual world. Scientific Reports, 4, Art 6526.Google Scholar
  9. Fuchs, B., & Thurner, S. (2014). Behavioral and network origins of wealth inequality: Insights from a virtual world. PLoS ONE, 9(8), e103503.ADSCrossRefGoogle Scholar
  10. Goh, K., & Barabási, A. L. (2008). Burstiness and memory in complex systems. Europhysics Letters, 81, 48002.ADSMathSciNetCrossRefGoogle Scholar
  11. Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360.CrossRefGoogle Scholar
  12. Heider, F. (1946). Attitudes and cognitive organization. Journal of Psychology, 21(2), 107–112.CrossRefGoogle Scholar
  13. Jo, H.-H., Karsai, M., Kertész, J., & Kaski, K. (2012). Circadian pattern and burstiness in mobile phone communication. New Journal of Physics, 14, 013055.ADSCrossRefGoogle Scholar
  14. Kenna, R., & Berche, B. (2010). The extensive nature of group quality. Europhysics Letters, 90, 58002.ADSCrossRefGoogle Scholar
  15. Klimek, P., & Thurner, S. (2013). Triadic closure dynamics drives scaling laws in social multiplex networks. New Journal of Physics, 15, 063008.ADSMathSciNetCrossRefGoogle Scholar
  16. Leskovec, J., Kleinberg, J., & Faloutsos, C. (2007). Graph evolution: Densification and shrinking diameters. ACM Transactions on Knowledge Discovery from Data, 1(1), 2.CrossRefGoogle Scholar
  17. Lim, M., Metzler, R., & Bar-Yam, Y. (2007). Global pattern formation and ethnic/cultural violence. Science, 317, 1540.ADSCrossRefGoogle Scholar
  18. Mac Carron, P., & Kenna, R. (2012). Universal properties of mythological networks. Europhysics Letters, 99, 28002.ADSCrossRefGoogle Scholar
  19. MacCarron, P., & Kenna, R. (2013). Network analysis of the Islendinga sogur - The Sagas of Icelanders. European Physical Journal B, 86, 407.ADSCrossRefGoogle Scholar
  20. Malmgren, R. D., Stouffer, D. B., Campanharo, A. S. L. O., & Amaral, L. A. N. (2009). On universality in human correspondence activity. Science, 325, 1696.ADSCrossRefGoogle Scholar
  21. Mryglod, O., Fuchs, B., Szell, M., Holovatch, Yu., & Thurner, S. (2015). Interevent time distributions of human multi-level activity in a virtual world. Physica A, 419, 681.ADSCrossRefGoogle Scholar
  22. Oliveira, J. G., & Barabási, A. (2005). Darwin and Einstein correspondence patterns. Nature, 437, 1251.ADSCrossRefGoogle Scholar
  23. Pardus. (2015). Web-page of the Pardus game. Retrieved May 14, 2015,
  24. Rapoport, A. (1953). Spread of information through a population with socio-structural bias. I. Assumption of transitivity. Bulletin of Mathematical Biology, 15(4), 523–533.MathSciNetGoogle Scholar
  25. Sinatra, R., Szell, M. (2014). Entropy and the predictability of online life. Entropy, 16, 543.ADSCrossRefGoogle Scholar
  26. Statista. (2015). Web-portal Statista (2015). World of WarCraft subscribers by quarter. Retrieved May 14, 2015,
  27. Stiller, J., Nettle, D., & Dunbar, R. I. M. (2003). Human nature (Vol. 14, No. 4, pp. 397–408). New York: Walter de Gruyter, Inc.Google Scholar
  28. Szell, M., Lambiotte, R., & Thurner, S. (2010). Multirelational organization of large-scale social networks in an online world. Proceedings of the National Academy of Sciences of the United States of America, 107, 13636.ADSCrossRefGoogle Scholar
  29. Szell, M., Sinatra, R., Petri, G., Thurner, S., & Latora, V. (2012). Understanding mobility in a social petri dish. Scientific Reports, 2, 457.ADSCrossRefGoogle Scholar
  30. Szell, M., & Thurner, S. (2010). Measuring social dynamics in a massive multiplayer online game. Social Networks, 32, 313.CrossRefGoogle Scholar
  31. Szell, M., & Thurner, S. (2013). How women organize social networks different from men. Scientific Reports, 3, 1214.ADSCrossRefGoogle Scholar
  32. Thurner, S., Szell, M., & Sinatra, R. (2012). Emergence of good conduct, scaling and Zipf laws in human behavioral sequences in an online world. PLoS ONE, 7(1), e29796.ADSCrossRefGoogle Scholar
  33. Vazquez, A., Oliveira, J. G., Dezso, Z., Goh, K. I., Kondor, I., & Barabasi, A. L. (2006). Modeling bursts and heavy tails in human dynamics. Physical Review E, 73(3), 036127.ADSCrossRefGoogle Scholar
  34. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (pp. 37–48). Cambridge: Cambridge University Press.CrossRefzbMATHGoogle Scholar
  35. Wu, Y., Zhou, C., Xiao, J., Kurths, J., & Schellnhuber, H. J. (2010). Evidence for a bimodal distribution in human communication. Proceedings of the National Academy of Sciences of the United States of America, 107, 18803.ADSCrossRefGoogle Scholar
  36. Yasseri, T., Sumi, R., & Kertész, J. (2012a). Circadian patterns of Wikipedia editorial activity: A demographic analysis. PLoS ONE 7(1), e30091.ADSCrossRefGoogle Scholar
  37. Yasseri, T., Sumi, R., Rung, A., Kornai, A., & Kertész, J. (2012b). Dynamics of conflicts in Wikipedia. PLoS ONE 7(6), e38869.ADSCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Yurij Holovatch
    • 1
  • Olesya Mryglod
    • 1
    Email author
  • Michael Szell
    • 2
  • Stefan Thurner
    • 3
    • 4
    • 5
  1. 1.Institute for Condensed Matter PhysicsNational Academy of Sciences of UkraineLvivUkraine
  2. 2.Center for Complex Network ResearchNortheastern UniversityBostonUSA
  3. 3.Section for Science of Complex SystemsMedical University of ViennaViennaAustria
  4. 4.Santa Fe InstituteSanta FeUSA
  5. 5.IIASALaxenburgAustria

Personalised recommendations