International Workshop on Modeling Social Media
International Workshop on Mining Ubiquitous and Social Environments
International Workshop on Machine Learning for Urban Sensor Data

Big Data Analytics in the Social and Ubiquitous Context pp 90-108 | Cite as

Formation and Temporal Evolution of Social Groups During Coffee Breaks

  • Martin Atzmueller
  • Andreas Ernst
  • Friedrich Krebs
  • Christoph Scholz
  • Gerd Stumme
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9546)


Group formation and evolution are prominent topics in social contexts. This paper focuses on the analysis of group evolution events in networks of face-to-face proximity. We first analyze statistical properties of group evolution, e.g., individual activity and typical group sizes. After that, we define a set of specific group evolution events. These are analyzed in the context of an academic conference, where we provide different patterns according to phases of the conference. Specifically, we investigate group formation and evolution using real-world data collected at the LWA 2010 conference utilizing the Conferator system, and discuss patterns according to different phases of the conference.


  1. 1.
    Atzmueller, M.: Knowledge-Intensive Subgroup Mining - Techniques for Automatic and Interactive Discovery. DISKI, vol. 307. IOS Press, The Netherlands (2007)Google Scholar
  2. 2.
    Atzmueller, M.: WIREs: subgroup discovery - advanced review. Data Min. Knowl. Discov. 5(1), 35–49 (2015)CrossRefGoogle Scholar
  3. 3.
    Atzmueller, M., Becker, M., Doerfel, S., Kibanov, M., Hotho, A., Macek, B.-E., Mitzlaff, F., Mueller, J., Scholz, C., Stumme, G.: Ubicon: observing social and physical activities. In: Proceedings of the 4th IEEE International Conference on Cyber, Physical and Social Computing (CPSCom 2012) (2012)Google Scholar
  4. 4.
    Atzmueller, M., Becker, M., Kibanov, M., Scholz, C., Doerfel, S., Hotho, A., Macek, B.-E., Mitzlaff, F., Mueller, J., Stumme, G.: Ubicon and its applications for ubiquitous social computing. New Rev. Hypermedia Multimedia 20(1), 53–77 (2014)CrossRefGoogle Scholar
  5. 5.
    Atzmueller, M., Benz, D., Doerfel, S., Hotho, A., Jäaschke, R., Macek, B.E., Mitzlaff, F., Scholz, C., Stumme, G.: Enhancing social interactions at conferences. it+ti 53(3), 101–107 (2011)Google Scholar
  6. 6.
    Atzmueller, M., Doerfel, S., Stumme, G., Mitzlaff, F., Hotho, A.: Face-to-face contacts at a conference: dynamics of communities and roles. In: Atzmueller, M., Chin, A., Helic, D., Hotho, A. (eds.) MUSE 2011 and MSM 2011. LNCS, vol. 7472, pp. 21–39. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Atzmueller, M., Doerfel, S., Mitzlaff, F.: Description-oriented community detection using exhaustive subgroup discovery. Inf. Sci. 329, 965–984 (2016, to appear)Google Scholar
  8. 8.
    Atzmueller, M., Hilgenberg, K.: Towards capturing social interactions with SDCF: an extensible framework for mobile sensing and ubiquitous data collection. In: Proceedings of the 4th International Workshop on Modeling Social Media (MSM 2013), Hypertext 2013. ACM Press, New York (2013)Google Scholar
  9. 9.
    Atzmueller, M., Lemmerich, F.: VIKAMINE – open-source subgroup discovery, pattern mining, and analytics. In: Bie, T., Cristianini, N., Flach, P.A. (eds.) ECML PKDD 2012, Part II. LNCS, vol. 7524, pp. 842–845. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Atzmueller, M., Lemmerich, F.: Exploratory pattern mining on social media using geo-references and social tagging information. IJWS 2(1/2), 80–112 (2013)CrossRefGoogle Scholar
  11. 11.
    Atzmueller, M., Lemmerich, F., Krause, B., Hotho, A.: Who are the spammers? understandable local patterns for concept description. In: Proceedings of the 7th Conference on Computer Methods and Systems. Oprogramowanie Nauko-Techniczne, Krakow, Poland (2009)Google Scholar
  12. 12.
    Atzmueller, M., Puppe, F.: A case-based approach for characterization and analysis of subgroup patterns. J. Appl. Intell. 28(3), 210–221 (2008)CrossRefGoogle Scholar
  13. 13.
    Atzmueller, M., Roth-Berghofer, T.: The mining and analysis continuum of explaining uncovered. In: Proceedings of the 30th SGAI International Conference on Artificial Intelligence (AI-2010) (2010)Google Scholar
  14. 14.
    Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: membership, growth, and evolution. In: Proceedings of the KDD, pp. 44–54. ACM, New York (2006)Google Scholar
  15. 15.
    Barrat, A., Cattuto, C.: Temporal Networks. Understanding Complex Systems. Springer, Heidelberg (2013). Temporal Networks of Face-to-Face Human InteractionsGoogle Scholar
  16. 16.
    Barrat, A., Cattuto, C., Colizza, V., Pinton, J.-F., den Broeck, W.V., Vespignani, A.: High Resolution Dynamical Mapping of Social Interactions with Active RFID (2008). CoRR, abs/0811.4170Google Scholar
  17. 17.
    Bojars, U., Breslin, J.G., Peristeras, V., Tummarello, G., Decker, S.: Interlinking the social web with semantics. IEEE Intell. Syst. 23(3), 29–40 (2008)CrossRefGoogle Scholar
  18. 18.
    Borgatti, S.P., Mehra, A., Brass, D.J., Labianca, G.: Network analysis in the social sciences. Science 323(5916), 892–895 (2009)CrossRefGoogle Scholar
  19. 19.
    Kołoszczyk, B., Kazienko, P., Bródka, P.: Predicting group evolution in the social network. In: Aberer, K., Flache, A., Jager, W., Liu, L., Tang, J., Guéret, C. (eds.) SocInfo 2012. LNCS, vol. 7710, pp. 54–67. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  20. 20.
    Bródka, P., Saganowski, S., Kazienko, P.: GED: the method for group evolution discovery in social networks. SNAM 3(1), 1–14 (2011)Google Scholar
  21. 21.
    Brown, C., Efstratiou, C., Leontiadis, I., Quercia, D., Mascolo, C.: Tracking serendipitous interactions: how individual cultures shape the office. In: Proceedings of the CSCW, pp. 1072–1081. ACM, New York (2014)Google Scholar
  22. 22.
    Cattuto, C.: Oral communication, 1 December 2014Google Scholar
  23. 23.
    Cattuto, C., Van den Broeck, W., Barrat, A., Colizza, V., Pinton, J.-F., Vespignani, A.: Dynamics of person-to-person interactions from distributed RFID sensor networks. PLoS ONE 5(7), e11596 (2010)CrossRefGoogle Scholar
  24. 24.
    Coleman, J.: Foundations of Social Theory. Belknap Press of Harvard University Press, Cambridge (2000)Google Scholar
  25. 25.
    Diakidis, G., Karna, D., Fasarakis-Hilliard, D., Vogiatzis, D., Paliouras, G.: Predictingthe evolution of communities in social networks. In: Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics, WIMS 2015, pp. 1:1–1:6. ACM, NewYork (2015)Google Scholar
  26. 26.
    Eagle, N., Pentland, A., Lazer, D.: From the cover: inferring friendship network structure by using mobile phone data. PNAS 106, 15274–15278 (2009)CrossRefGoogle Scholar
  27. 27.
    Fortunato, S., Castellano, C.: Encyclopedia of Complexity and System Science. Springer, New York (2007). Community Structure in GraphsGoogle Scholar
  28. 28.
    Isella, L., Romano, M., Barrat, A., Cattuto, C., Colizza, V., Van den Broeck, W., Gesualdo, F., Pandolfi, E., Ravà, L., Rizzo, C., Tozzi, A.: Close encounters in a pediatric ward: measuring face-to-face proximity and mixing patterns with wearable sensors. PLoS ONE 6, e17144 (2011)CrossRefGoogle Scholar
  29. 29.
    Isella, L., Stehlé, J., Barrat, A., Cattuto, C., Pinton, J.-F., Broeck, W.V.D.: What’s in a crowd? analysis of face-to-face behavioral networks. J. Theor. Biol. 271, 166–180 (2011)CrossRefGoogle Scholar
  30. 30.
    Kibanov, M., Atzmueller, M., Illig, J., Scholz, C., Barrat, A., Cattuto, C., Stumme, G.: Is web content a good proxy for real-life interaction? a case study considering online and offline interactions of computer scientists. In: Proceedings of the ASONAM. IEEE Press, Boston (2015)Google Scholar
  31. 31.
    Kibanov, M., Atzmueller, M., Scholz, C., Stumme, G.: Temporal evolution of contacts and communities in networks of face-to-face human interactions. Sci. Chin. 57, 1–17 (2014)Google Scholar
  32. 32.
    Kim, J., Lee, J.-E.R.: The facebook paths to happiness: effects of the number of facebook friends and self-presentation on subjective well-being. Cyberpsychol. Behav. Soc. Netw. 14(6), 359–364 (2011)CrossRefGoogle Scholar
  33. 33.
    Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In: Yu, P.S., Han, J., Faloutsos, C. (eds.) Link Mining: Models Algorithms, and Applications, pp. 337–357. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  34. 34.
    Macek, B.-E., Scholz, C., Atzmueller, M., Stumme, G.: Anatomy of a conference. In: Proceedings of the ACM Hypertext, New York, pp. 245–254 (2012)Google Scholar
  35. 35.
    Machens, A., Gesualdo, F., Rizzo, C., Tozzi, A.E., Barrat, A., Cattuto, C.: An Infectious Disease Model on Empirical Networks of Human Contact: Bridging the Gap between Dynamic Network Data and Contact Matrices. BMC Infectious Diseases 13(185) (2013)Google Scholar
  36. 36.
    Hotho, A., Atzmueller, M., Stumme, G., Benz, D., Mitzlaff, F.: Community assessment using evidence networks. In: Atzmueller, M., Hotho, A., Strohmaier, M., Chin, A. (eds.) MUSE/MSM 2010. LNCS, vol. 6904, pp. 79–98. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  37. 37.
    Atzmueller, M., Benz, D., Hotho, A., Mitzlaff, F., Stumme, G.: Community assessment using evidence networks. In: Atzmueller, M., Hotho, A., Strohmaier, M., Chin, A. (eds.) MUSE/MSM 2010. LNCS, vol. 6904, pp. 79–98. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  38. 38.
    Mitzlaff, F., Atzmueller, M., Benz, D., Hotho, A., Stumme, G.: User-Relatedness and Community Structure in Social Interaction Networks (2013). CoRR, abs/1309.3888Google Scholar
  39. 39.
    Mitzlaff, F., Atzmueller, M., Hotho, A., Stumme, G.: The social distributional hypothesis. J. Soc. Netw. Anal. Min. 4(216) (2014)Google Scholar
  40. 40.
    Atzmueller, M., Stumme, G., Hotho, A., Mitzlaff, F.: Semantics of user interaction in social media. In: Ghoshal, G., Poncela-Casasnovas, J., Tolksdorf, R. (eds.) Complex Networks IV. SCI, vol. 476, pp. 13–25. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  41. 41.
    Newman, M.E.J.: Detecting community structure in networks. Eur. Phys. J. 38, 321–330 (2004)CrossRefGoogle Scholar
  42. 42.
    Palla, G., Barabasi, A.-L., Vicsek, T.: Quantifying social group evolution. Nature 446(7136), 664–667 (2007)CrossRefGoogle Scholar
  43. 43.
    Parra, D., Trattner, C., Gómez, D., Hurtado, M., Wen, X., Lin, Y.-R.: Twitter in academic events: a study of temporal usage, communication, sentimental and topical patterns in 16 computer science conferences. Comput. Commun. 73, 301–314 (2015)CrossRefGoogle Scholar
  44. 44.
    Saganowski, S., Gliwa, B., Bródka, P., Zygmunt, A., Kazienko, P., Kozlak, J.: Predicting community evolution in social networks. Entropy 17, 3053–3096 (2015)CrossRefGoogle Scholar
  45. 45.
    Scholz, C., Atzmueller, M., Barrat, A., Cattuto, C., Stumme, G.: New insights and methods for predicting face-to-face contacts. In: Proceedings of the 7th International AAAI Conference on Weblogs and Social Media. AAAI Press, Palo Alto (2013)Google Scholar
  46. 46.
    Scholz, C., Atzmueller, M., Stumme, G.: On the predictability of human contacts: influence factors and the strength of stronger ties. In: Proceedings of the Fourth ASE/IEEE International Conference on Social Computing. IEEE Computer Society, Boston (2012)Google Scholar
  47. 47.
    Scholz, C., Atzmueller, M., Stumme, G.: Unsupervised and hybrid approaches for on-line RFID localization with mixed context knowledge. In: Andreasen, T., Christiansen, H., Cubero, J.-C., Raś, Z.W. (eds.) ISMIS 2014. LNCS, vol. 8502, pp. 244–253. Springer, Heidelberg (2014)Google Scholar
  48. 48.
    Scholz, C., Stumme, G., Doerfel, S., Hotho, A., Atzmueller, M.: Resource-aware on-line RFID localization using proximity data. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part III. LNCS, vol. 6913, pp. 129–144. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  49. 49.
    Scholz, C., Illig, J., Atzmueller, M., Stumme, G.: On the Predictability of talk attendance at academic conferences. In: Proceedings of the 25th ACM Conference on Hypertext and Social Media. ACM Press, New York (2014)Google Scholar
  50. 50.
    Staab, S.: Emergent semantics. IEEE Intell. Syst. 1, 78–86 (2002)CrossRefGoogle Scholar
  51. 51.
    Stehlé, J., Voirin, N., Barrat, A., Cattuto, C., Isella, L., Pinton, J.-F., Quaggiotto, M., Van den Broeck, W., Régis, C., Lina, B., Vanhems, P.: High-resolution measurements of face-to-face contact patterns in a primary school. PLoS ONE 6(8), e23176 (2011)CrossRefGoogle Scholar
  52. 52.
    Steurer, M., Trattner, C.: Predicting interactions in online social networks: an experiment in second life. In: Proceedings of the 4th International Workshop on Modeling Social Media, MSM 2013, pp. 5:1–5:8. ACM, New York (2013)Google Scholar
  53. 53.
    Subrahmanyam, K., Reich, S.M., Waechter, N., Espinoza, G.: Online and offline social networks: use of social networking sites by emerging adults. J. Appl. Dev. Psychol. 29(6), 420–433 (2008)CrossRefGoogle Scholar
  54. 54.
    Tang, L., Liu, H., Zhang, J., Nazeri, Z.: Community evolution in dynamic multi-mode networks. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 677–685. ACM, New York (2008)Google Scholar
  55. 55.
    Tang, L., Wang, X., Liu, H.: Community detection via heterogeneous interaction analysis. Data Min. Knowl. Discov. 25(1), 1–33 (2012)MathSciNetCrossRefGoogle Scholar
  56. 56.
    Turner, J.C.: Towards a cognitive redefinition of the social group. Cah. Psychol. Cogn. 1(2), 93–118 (1981)Google Scholar
  57. 57.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Structural analysis in the social sciences, vol. 8, 1st edn. Cambridge University Press, Cambridge (1994)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Martin Atzmueller
    • 1
  • Andreas Ernst
    • 2
  • Friedrich Krebs
    • 2
  • Christoph Scholz
    • 3
  • Gerd Stumme
    • 1
  1. 1.Knowledge and Data Engineering Group, Research Center for Information System DesignUniversity of KasselKasselGermany
  2. 2.Center for Environmental Systems ResearchUniversity of KasselKasselGermany
  3. 3.Department Energy Informatics and Information SystemsFraunhofer Institute for Wind Energy and Energy System TechnologyKasselGermany

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