Local Exceptionality Detection on Social Interaction Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9853)

Abstract

Local exceptionality detection on social interaction networks includes the analysis of resources created by humans (e. g., social media) as well as those generated by sensor devices in the context of (complex) interactions. This paper provides a structured overview on a line of work comprising a set of papers that focus on data-driven exploration and modeling in the context of social network analysis, community detection and pattern mining.

Keywords

Local exceptionality detection Exceptional models Subgroup discovery Community detection Social network analysis Social interaction networks Social media 

References

  1. 1.
    Atzmueller, M.: Mining social media: key players, sentiments, and communities. WIREs Data Min. Knowl. Discovery (DMKD) 2(5), 411–419 (2012)CrossRefGoogle Scholar
  2. 2.
    Atzmueller, M.: Data mining on social interaction networks. JDMDH 29, 1–21 (2014)Google Scholar
  3. 3.
    Atzmueller, M.: Subgroup discovery. WIREs DMKD 5(1), 35–49 (2015)Google Scholar
  4. 4.
    Atzmueller, M.: Detecting community patterns capturing exceptional link trails. In: Proceedings IEEE/ACM ASONAM. IEEE Press, Boston, MA, USA (2016)Google Scholar
  5. 5.
    Atzmueller, M., Doerfel, S., Mitzlaff, F.: Description-oriented community detection using exhaustive subgroup discovery. Inf. Sci. 329, 965–984 (2016)CrossRefGoogle Scholar
  6. 6.
    Atzmueller, M., Lemmerich, F.: Exploratory pattern mining on social media using geo-references and social tagging information. IJWS 2(1/2), 80–112 (2013)Google Scholar
  7. 7.
    Atzmueller, M., Mollenhauer, D., Schmidt, A.: Big Data analytics using local exceptionality detection. In: Enterprise Big Data Engineering, Analytics, and Management. IGI Global, Hershey, PA, USA (2016)Google Scholar
  8. 8.
    Atzmueller, M., Mueller, J., Becker, M.: Exploratory subgroup analytics on ubiquitous data. In: Atzmueller, M., Chin, A., Scholz, C., Trattner, C. (eds.) MUSE/MSM 2013, LNAI 8940. LNCS, vol. 8940, pp. 1–20. Springer, Heidelberg (2015)Google Scholar
  9. 9.
    Atzmueller, M., Roth-Berghofer, T.: The mining and analysis continuum of explaining uncovered. In: Proceedings 30th SGAI International Conference on Artificial Intelligence (2010)Google Scholar
  10. 10.
    Atzmueller, M., Schmidt, A., Kibanov, M.: DASHTrails: an approach for modeling and analysis of distribution-adapted sequential hypotheses and trails. In: Proceedings WWW 2016 (Companion). IW3C2/ACM (2016)Google Scholar
  11. 11.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  12. 12.
    Duivesteijn, W., Feelders, A.J., Knobbe, A.: Exceptional model mining. Data Min. Knowl. Discovery 30(1), 47–98 (2016)MathSciNetCrossRefGoogle Scholar
  13. 13.
    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 IEEE/ACM ASONAM. ACM (2015)Google Scholar
  14. 14.
    Kibanov, M., Atzmueller, M., Scholz, C., Stumme, G.: Temporal evolution of contacts and communities in networks of face-to-face human interactions. Sci. China Inf. Sci. 57, 32103 (2014)CrossRefGoogle Scholar
  15. 15.
    Klösgen, W.: Explora: a multipattern and multistrategy discovery assistant. In: Advances in Knowledge Discovery and Data Mining, pp. 249–271. AAAI Press (1996)Google Scholar
  16. 16.
    Mannila, H.: Theoretical frameworks for data mining. SIGKDD Explor. 1(2), 30–32 (2000)CrossRefGoogle Scholar
  17. 17.
    Mitzlaff, F., Atzmueller, M., Benz, D., Hotho, A., 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
  18. 18.
    Mitzlaff, F., Atzmueller, M., Benz, D., Hotho, A., Stumme, G.: User-Relatedness and Community Structure in Social Interaction Networks. CoRR/abs 1309.3888 (2013)Google Scholar
  19. 19.
    Mitzlaff, F., Atzmueller, M., Hotho, A., Stumme, G.: The social distributional hypothesis. J. Soc. Netw. Anal. Min. 4(216), 1–14 (2014)Google Scholar
  20. 20.
    Morik, K.: Detecting interesting instances. In: Hand, D.J., Adams, N.M., Bolton, R.J. (eds.) Pattern Detection and Discovery. LNCS (LNAI), vol. 2447, pp. 13–23. Springer, Heidelberg (2002). doi:10.1007/3-540-45728-3_2 CrossRefGoogle Scholar
  21. 21.
    Scholz, C., Atzmueller, M., Barrat, A., Cattuto, C., Stumme, G.: New insights and methods for predicting face-to-face contacts. In: Proceedings ICWSM. AAAI, Palo Alto, CA, USA (2013)Google Scholar
  22. 22.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. No. 8 in Structural Analysis in the Social Sciences, 1st edn. Cambridge University Press, New York (1994)CrossRefMATHGoogle Scholar
  23. 23.
    Wrobel, S.: An algorithm for multi-relational discovery of subgroups. In: Komorowski, J., Zytkow, J. (eds.) PKDD 1997. LNCS, vol. 1263, pp. 78–87. Springer, Heidelberg (1997). doi:10.1007/3-540-63223-9_108

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Research Center for Information System Design (ITeG)University of KasselKasselGermany

Personalised recommendations