Social Interaction Discovery: A Simulated Multiagent Approach

  • José C. Carrasco-Jiménez
  • José M. Celaya-Padilla
  • Gilberto Montes
  • Ramón F. Brena
  • Sigfrido Iglesias
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7914)


Social interaction inference is a problem that has been of interest in the past few years. The intrinsic mobility patterns followed by humans present a number of challenges that range from interaction inference to identification of social relationships linking individuals. An intuitive approach is to focus on the similarity of mobility patterns as an indicator of possible social interaction among individuals. By recording the access points observed at each unit of time along with the strength of the signals received, individuals may be group based on similar walking patterns shared on space and time. In this paper, an implementation of a multiagent simulation of a University-like environment is tested using NetLogo and a methodology that consists of two phases: 1) Cluster Analysis and 2) Construction of Social Networks is used to discover possible interactions among individuals. The first phase consists of a number of clustering methods that are used to identify individuals that are more closely related given the characteristics that describe their mobility patterns obtained from simulated Wifi data. In the second phase, users belonging to the same cluster are linked within a social network, meaning that there is possible ongoing social interaction or tie that might link the individuals.


  1. 1.
    Cranshaw, J., Toch, E., Hong, J., Kittur, A., Sadeh, N.: Bridging the gap between physical location and online social networks. In: Proceedings of the 12th ACM International Conference on Ubiquitous Computing, Ubicomp 2010, pp. 119–128. ACM, New York (2010)CrossRefGoogle Scholar
  2. 2.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer Series in Statistics. Springer New York Inc., New York (2001)zbMATHGoogle Scholar
  3. 3.
    Witten, I., Frank, E., Hall, M.: Data Mining: Practical Machine Learning Tools and Techniques: Practical Machine Learning Tools and Techniques. The Morgan Kaufmann Series in Data Management Systems. Elsevier Science (2011)Google Scholar
  4. 4.
    R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2012) ISBN 3-900051-07-0Google Scholar
  5. 5.
    Hennig, C.: fpc: Flexible procedures for clustering, R package version 2.1-4 (2012)Google Scholar
  6. 6.
    Patwary, M.A., Palsetia, D., Agrawal, A., Liao, W.K., Manne, F., Choudhary, A.: A new scalable parallel dbscan algorithm using the disjoint-set data structure. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC 2012, pp. 62:1–62:11. IEEE Computer Society Press, Los Alamitos (2012)Google Scholar
  7. 7.
    Eagle, N., Pentland, A., Lazer, D.: From the cover: Inferring friendship network structure by using mobile phone data. Proceedings of The National Academy of Sciences 106, 15274–15278 (2009)CrossRefGoogle Scholar
  8. 8.
    Mokhtar, S.B., McNamara, L., Capra, L.: A middleware service for pervasive social networking. In: Proceedings of the International Workshop on Middleware for Pervasive Mobile and Embedded Computing, M-PAC 2009, pp. 2:1–2:6. ACM, New York (2009)Google Scholar
  9. 9.
    Xu, B., Chin, A., Wang, H., Wang, H., Zhang, L.: Social linking and physical proximity in a mobile location-based service. In: Proceedings of the 1st International Workshop on Mobile Location-Based Service, MLBS 2011, pp. 99–108. ACM, New York (2011)CrossRefGoogle Scholar
  10. 10.
    Zhu, L., Chin, A., Zhang, K., Xu, W., Wang, H., Zhang, L.: Managing workplace resources in office environments through ephemeral social networks. In: Yu, Z., Liscano, R., Chen, G., Zhang, D., Zhou, X. (eds.) UIC 2010. LNCS, vol. 6406, pp. 665–679. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Tisue, S., Wilensky, U.: Netlogo: A simple environment for modeling complexity. In: International Conference on Complex Systems, pp. 16–21 (2004)Google Scholar
  12. 12.
    CC2431 Location EngineGoogle Scholar
  13. 13.
    Bose, A., Foh, C.H.: A practical path loss model for indoor wifi positioning enhancement. In: 2007 6th International Conference on Information, Communications Signal Processing, pp. 1–5 (December 2007)Google Scholar
  14. 14.
    Csardi, G., Nepusz, T.: The igraph software package for complex network research. Inter. Journal Complex Systems 1695 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • José C. Carrasco-Jiménez
    • 1
  • José M. Celaya-Padilla
    • 1
  • Gilberto Montes
    • 1
  • Ramón F. Brena
    • 1
  • Sigfrido Iglesias
    • 1
  1. 1.Instituto Tecnológico y de Estudios Superiores de MonterreyMonterreyMéxico

Personalised recommendations