Predicting Guild Membership in Massively Multiplayer Online Games

  • Hamidreza Alvari
  • Kiran Lakkaraju
  • Gita Sukthankar
  • Jon Whetzel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8393)


Massively multiplayer online games (MMOGs) offer a unique laboratory for examining large-scale patterns of human behavior. In particular, the study of guilds in MMOGs has yielded insights about the forces driving the formation of human groups. In this paper, we present a computational model for predicting guild membership in MMOGs and evaluate the relative contribution of 1) social ties, 2) attribute homophily, and 3) existing guild membership toward the accuracy of the predictive model. Our results indicate that existing guild membership is the best predictor of future membership; moreover knowing the identity of a few influential members, as measured by network centrality, is a more powerful predictor than a larger number of less influential members. Based on these results, we propose that community detection algorithms for virtual worlds should exploit publicly available knowledge of guild membership from sources such as profiles, bulletin boards, and chat groups.


group formation MMOGs community detection homophily 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Huang, Y., Zhu, M., Wang, J., Pathak, N., Shen, C., Keegan, B., Williams, D., Contractor, N.: The formation of task-oriented groups: Exploring combat activities in online games. In: IEEE International Conference on Social Computing (2009)Google Scholar
  2. 2.
    Williams, D.: The mapping principle, and a research framework for virtual worlds. Communication Theory 20(4), 451–470 (2010)CrossRefGoogle Scholar
  3. 3.
    Johnson, N., Xu, C., Zhao, Z., Duchenaut, N., Yee, N., Tita, G., Hui, P.: Human group formation in online guilds and offline gangs driven by a common team dynamic. Physical Review E (2009)Google Scholar
  4. 4.
    Thurau, C., Bauckhage, C.: Analyzing the evolution of social groups in World of Warcraft. In: IEEE International Conference on Computational Intelligence in Games, pp. 170–177 (2010)Google Scholar
  5. 5.
    Ahmad, M.A., Borbora, Z., Shen, C., Srivastava, J., Williams, D.: Guild play in mMOGs: Rethinking common group dynamics models. In: Datta, A., Shulman, S., Zheng, B., Lin, S.-D., Sun, A., Lim, E.-P. (eds.) SocInfo 2011. LNCS, vol. 6984, pp. 145–152. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  6. 6.
    Williams, D., Ducheneaut, N., Xiong, L., Zhang, Y., Yee, N., Nickell, E.: From tree house to barracks: The social life of guilds in World of Warcraft. Games and Culture 1(4), 338–361 (2006)CrossRefGoogle Scholar
  7. 7.
    Lakkaraju, K., Whetzel, J.: Group roles in massively multiplayer online games. In: Proceedings of the Workshop on Collaborative Online Organizations at the 14th International Conference on Autonomous Agents and Multi-Agent Systems (2013)Google Scholar
  8. 8.
    Castronova, E., Williams, D., Shen, C., Ratan, R., Xiong, L., Huang, Y., Keegan, B.: As real as real? macroeconomic behavior in a large-scale virtual world. New Media and Society 11(5), 685–707 (2009)CrossRefGoogle Scholar
  9. 9.
    Shah, F., Sukthankar, G.: Using network structure to identify groups in virtual worlds. In: Proceedings of the International AAAI Conference on Weblogs and Social Media, Barcelona, Spain, pp. 614–617 (July 2011)Google Scholar
  10. 10.
    Pang, S., Chen, C.: Community analysis of social networks in MMOG. Communications, Network and System Sciences 3, 133–139 (2010)CrossRefGoogle Scholar
  11. 11.
    Alvari, H., Hashemi, S., Hamzeh, A.: Detecting overlapping communities in social networks by game theory and structural equivalence concept. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds.) AICI 2011, Part II. LNCS, vol. 7003, pp. 620–630. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Chen, C., Sun, C., Hsieh, J.: Player guild dynamics and evolution in massively multiplayer online games. Cyber Psychology and Behavior 11(3) (2008)Google Scholar
  13. 13.
    Alvari, H., Hashemi, S., Hamzeh, A.: Discovering overlapping communities in social networks: a novel game-theoretic approach. AI Communications 36(2), 161–177 (2013)MathSciNetGoogle Scholar
  14. 14.
    Wasserman, S.: Social Network Analysis: Methods and Applications. Cambridge University Press (1994)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hamidreza Alvari
    • 1
  • Kiran Lakkaraju
    • 2
  • Gita Sukthankar
    • 1
  • Jon Whetzel
    • 2
  1. 1.University of Central FloridaOrlandoUSA
  2. 2.Sandia National LabsAlbuquerqueUSA

Personalised recommendations