Science China Information Sciences

, Volume 57, Issue 3, pp 1–17 | Cite as

Temporal evolution of contacts and communities in networks of face-to-face human interactions

  • Mark Kibanov
  • Martin Atzmueller
  • Christoph Scholz
  • Gerd Stumme
Research Paper Special Focus on Adv. Sci. & Tech. for Future Cybermatics


Temporal dynamics of social interaction networks as well as the analysis of communities are key aspects to gain a better understanding of the involved processes, important influence factors, their effects, and their structural implications. In this article, we analyze temporal dynamics of contacts and the evolution of communities in networks of face-to-face proximity. As our application context, we consider four scientific conferences. On a structural level, we focus on static and dynamic properties of the contact graphs. Also, we analyze the resulting community structure using state-of-the-art automatic community detection algorithms. Specifically, we analyze the evolution of contacts and communities over time to consider the stability of the respective communities. Furthermore, we assess different factors which have an influence on the quality of community prediction. Overall, we provide first important insights into the evolution of contacts and communities in face-to-face contact networks.


social network analysis community detection face-to-face contact networks temporal networks, community stability 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Mitzlaff F, Atzmueller M, Benz D, et al. User-relatedness and community structure in social interaction networks. arXiv:1309.3888, 2013Google Scholar
  2. 2.
    Atzmueller M, Becker M, Doerfel S, et al. Ubicon: observing physical and social activities. In: Proceedings of 2012 IEEE International Conference on Cyber, Physical and Social Computing (CPSCom). Piscataway: IEEE, 2012. 317–324CrossRefGoogle Scholar
  3. 3.
    Atzmueller M, Benz D, Doerfel S, et al. Enhancing Social Interactions at Conferences. Inf Technol, 2011, 53: 101–107Google Scholar
  4. 4.
    Barrat A, Cattuto C, Colizza V, et al. High resolution dynamical mapping of social interactions with active RFID. arXiv:0811.4170, 2008Google Scholar
  5. 5.
    Kibanov M, Atzmueller M, Scholz C, et al. On the evolution of contacts and communities in networks of face-to-face proximity. In: Proceedings of IEEE International Conference on Cyber, Physical and Social Computing. Piscataway: IEEE, 2013. 993–1000Google Scholar
  6. 6.
    Eagle N, Pentland A S, Lazer D. Inferring friendship network structure by using mobile phone data. Proc National Acad Sci, 2009, 106: 15274–15278CrossRefGoogle Scholar
  7. 7.
    Hui P, Chaintreau A, Scott J, et al. Pocket switched networks and human mobility in conference environments. In: Proceedings of the 2005 ACM SIGCOMM Workshop on Delay-Tolerant Networking. New York: ACM, 2005. 244–251CrossRefGoogle Scholar
  8. 8.
    Zuo X, Chin A, Fan X, et al. Connecting people at a conference: a study of influence between offline and online using a mobile social application. In: Proceedings of 2012 IEEE International Conference on Green Computing and Communications (GreenCom). Piscataway: IEEE, 2012. 277–284CrossRefGoogle Scholar
  9. 9.
    Meriac M, Fiedler A, Hohendorf A, et al. Localization techniques for a mobile museum information system. In: Proceedings of Wireless Communication and Information, Berlin, 2007Google Scholar
  10. 10.
    Cattuto C, van den Broeck W, Barrat A, et al. Dynamics of person-to-person interactions from distributed RFID sensor networks. PloS ONE, 2010, 5: e11596CrossRefGoogle Scholar
  11. 11.
    Alani H, Szomszor M, Cattuto C, et al. Live social semantics. In: Proceedings of International Semantic Web Conference 2009. Berlin: Springer, 2009. 698–714CrossRefGoogle Scholar
  12. 12.
    Barrat A, Cattuto C, Szomszor M, et al. Social dynamics in conferences: analyses of data from the live social semantics application. In: Proceedings of International Semantic Web Conference 2010. Berlin: Springer, 2010. 17–33CrossRefGoogle Scholar
  13. 13.
    Isella L, Romano M, Barrat A, et al. Close encounters in a pediatric ward: measuring face-to-face proximity and mixing patterns with wearable sensors. PLoS ONE, 2011, 6: e17144CrossRefGoogle Scholar
  14. 14.
    Machens A, Gesualdo F, Rizzo C, et al. An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices. BMC Infectious Diseases, 2013, 13: 185CrossRefGoogle Scholar
  15. 15.
    Stehlé J, Voirin N, Barrat A, et al. High-resolution measurements of face-to-face contact patterns in a primary school. PloS ONE, 2011, 6: e23176CrossRefGoogle Scholar
  16. 16.
    Isella L, Stehlé J, Barrat A, et al. What’s in a crowd? Analysis of face-to-face behavioral networks[J]. J Theor Biol, 2011, 271: 166–180CrossRefGoogle Scholar
  17. 17.
    Barrat A, Cattuto C. Temporal networks of face-to-face human interactions. In: Holme P, Saramaki J, eds. Temporal Networks. Berlin: Springer, 2013. 191–216CrossRefGoogle Scholar
  18. 18.
    Atzmueller M, Doerfel S, Hotho A, et al. Face-to-face contacts at a conference: dynamics of communities and roles. In: Atzmueller M, Chin A, Helic D, et al, eds. Modeling and Mining Ubiquitous Social Media. Berlin: Springer, 2011. 21–39Google Scholar
  19. 19.
    Macek B E, Scholz C, Atzmueller M, et al. Anatomy of a Conference. In: Proceedings of the 23rd ACM Conference on Hypertext and Social Media. New York: ACM, 2012. 245–254CrossRefGoogle Scholar
  20. 20.
    Scholz C, Atzmueller M, Stumme G, et al. New insights and methods for predicting face-to-face contacts. In: Proceedings of the 7th International AAAI Conference on Weblogs and Social Media, Boston, 2013. 563–572Google Scholar
  21. 21.
    Scholz C, Atzmueller M, Stumme G. On the predictability of human contacts: influence factors and the strength of stronger ties. In: Proceedings of the 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing. Piscataway: IEEE, 2012. 312–321CrossRefGoogle Scholar
  22. 22.
    Coleman J S. Foundations of Social Theory. Cambridge: Belknap Press of Harvard University Press, 2000Google Scholar
  23. 23.
    Wasserman S, Faust K. Social Network Analysis: Methods and Applications. New York: Cambridge University Press, 1994CrossRefGoogle Scholar
  24. 24.
    Palla G, Barabási A L, Vicsek T. Quantifying social group evolution. Nature, 2007, 446: 664–667CrossRefGoogle Scholar
  25. 25.
    Backstrom L, Huttenlocher D, Kleinberg J, et al. Group formation in large social networks: membership, growth, and evolution. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2006. 44–54CrossRefGoogle Scholar
  26. 26.
    Kumar R, Novak J, Raghavan P, et al. On the bursty evolution of blogspace. In: Proceedings of the 12th International Conference on World Wide Web, Budapest, 2003. 159–178Google Scholar
  27. 27.
    Holme P, Edling C R, Liljeros F. Structure and time evolution of an Internet dating community. Social Networks, 2004, 26: 155–174CrossRefGoogle Scholar
  28. 28.
    Asur S, Parthasarathy S, Ucar D. An event-based framework for characterizing the evolutionary behavior of interaction graphs. In: Proceedings of the 13th ACMSIGKDD International Conference on Knowledge Discovery and DataMining. New York: ACM, 2007. 913–921CrossRefGoogle Scholar
  29. 29.
    Fortunato S, Castellano C. Community structure in graphs. In: Mayers R A, eds. Computational Complexity. New York: Springer, 2012. 490–512CrossRefGoogle Scholar
  30. 30.
    Fortunato S, Lancichinetti A. Community detection algorithms: a comparative analysis. In: Proceedings of the 4th International Conference on Performance Evaluation Methodologies and Tools, Pisa, 2009. 27Google Scholar
  31. 31.
    Newman M E J, Girvan M. Finding and evaluating community structure in networks. Phys Rev E, 2004, 69: 026113CrossRefGoogle Scholar
  32. 32.
    Newman M E J. Detecting community structure in networks. Eur Phys J B Condens Matter Complex Syst, 2004, 38: 321–330CrossRefGoogle Scholar
  33. 33.
    Newman M E J. Modularity and community structure in networks. Proc National Acad Sci, 2006, 103: 8577–8582CrossRefGoogle Scholar
  34. 34.
    Lin Y R, Sun J, Sundaram H, et al. Community discovery via metagraph factorization. ACM Trans Knowl Discovery Data, 2011, 5: 17Google Scholar
  35. 35.
    Lin Y R, Chi Y, Zhu S, et al. Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. In: Proceedings of the 17th International Conference on World Wide Web, Beijing, 2008. 685–694CrossRefGoogle Scholar
  36. 36.
    Lin Y R, Chi Y, Zhu S, et al. Analyzing communities and their evolutions in dynamic social networks. ACM Trans Knowl Discovery Data, 2009, 3: 8:1–8:31Google Scholar
  37. 37.
    Leskovec J, Lang K J, Mahoney M. Empirical comparison of algorithms for network community detection. In: Proceedings of the 19th International Conference on World Wide Web, Raleigh, 2010. 631–640CrossRefGoogle Scholar
  38. 38.
    Leskovec J, Lang K J, Dasgupta A, et al. Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters. Internet Mathematics, 2009, 6: 29–123CrossRefMATHMathSciNetGoogle Scholar
  39. 39.
    Papadopoulos S, Kompatsiaris Y, Vakali A, et al. Community detection in social media. Data Mining Knowl Discovery, 2012, 24: 515–554CrossRefGoogle Scholar
  40. 40.
    Sun J, Faloutsos C, Papadimitriou S, et al. Graphscope: parameter-free mining of large time-evolving graphs. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, 2007. 2007. 687–696Google Scholar
  41. 41.
    Sundaram H, Lin Y R, de Choudhury M, et al. Understanding community dynamics in online social networks: a multidisciplinary review. Signal Process Mag, 2012, 29: 33–40CrossRefGoogle Scholar
  42. 42.
    Toyoda M, Kitsuregawa M. Extracting evolution of web communities from a series of web archives. In: Proceedings of the 14th ACM Conference on Hypertext and Hypermedia, Nottingham, 2003. 28–37Google Scholar
  43. 43.
    Kawadia V, Sreenivasan S. Sequential detection of temporal communities by estrangement confinement. Sci Rep, 2012, 2: 794CrossRefGoogle Scholar
  44. 44.
    Yang T, Chi Y, Zhu S, et al. Detecting communities and their evolutions in dynamic social networks — a Bayesian approach. Machine Learning, 2011, 82: 157–189CrossRefMATHMathSciNetGoogle Scholar
  45. 45.
    Rosvall M, Axelsson D, Bergstrom C T. The map equation. Eur Phys J Special Top, 2009, 178: 13–23CrossRefGoogle Scholar
  46. 46.
    Rosvall M, Bergstrom C T. Maps of random walks on complex networks reveal community structure. Proc National Acad Sci, 2008, 105: 1118–1123CrossRefGoogle Scholar
  47. 47.
    Raghavan U N, Albert R, Kumara S. Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E, 2007, 76: 036106CrossRefGoogle Scholar
  48. 48.
    Newman M E J. Finding community structure in networks using the eigenvectors of matrices. Phys Rev E, 2006, 74: 036104CrossRefMathSciNetGoogle Scholar
  49. 49.
    Pons P, Latapy M. Computing communities in large networks using random walks. In: Proceedings of the 20th International Conference on Computer and Information Sciences, Istanbul, 2005. 284–293Google Scholar
  50. 50.
    Clauset A, Newman M E J, Moore C. Finding community structure in very large networks. Phys Rev E, 2004, 70: 066111CrossRefGoogle Scholar
  51. 51.
    Szomszor M, Cattuto C, van den Broeck W, et al. Semantics, sensors, and the social web: the live social semantics experiments. In: Proceedings of the 7th Extended Semantic Web Conference, Heraklion, 2010. 196–210Google Scholar
  52. 52.
    Scholz C, Doerfel S, Atzmueller M, et al. Resource-aware on-line RFID localization using proximity data. In: Gunopulos D, Hofmann T, Malerba D, et al, eds. Machine Learning and Knowledge Discovery in Databases. Berlin: Springer, 2011. 129–144CrossRefGoogle Scholar
  53. 53.
    Atzmueller M, Mitzlaff F. Efficient descriptive community mining. In: Proceedings of the 24th International Florida Artificial Intelligence Research Society Conference, Palm Beach, 2011. 459–464Google Scholar

Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Mark Kibanov
    • 1
  • Martin Atzmueller
    • 1
  • Christoph Scholz
    • 1
  • Gerd Stumme
    • 1
  1. 1.Knowledge and Data Engineering GroupUniversity of KasselKasselGermany

Personalised recommendations