Advertisement

Identification of Patterns in Blogosphere Considering Social Positions of Users and Reciprocity of Relations

  • Krzysztof RudekEmail author
  • Jarosław Koźlak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10870)

Abstract

The aim of the paper is to identify and categorize frequent patterns describing interactions between users in social networks. We consider a social network with already identified relationships between users which evolves in time. The social network is based on the Polish blog website pertaining on socio-political issues salon24.pl. It consists of bloggers and links between them, which result from the intensity and characteristic features of posting comments. In our research, we discover patterns based on frequent and fast interactions between pairs of users. The patterns are described by the characteristics of these interactions, such as their reciprocity, the relative difference between estimates of global influence in the pairs of users participating in the discussions and time of day of the conversation. In addition, we consider the roles of system users, determined by the number of interactions initiating discussions, their frequency and the number of strong interactions in which users are involved. We take into account how many such intense conversations individual users participate in.

Keywords

Social network analysis Social relations Identification of social patterns Blogosphere 

Notes

Acknowledgments

This work is partially funded by the Dean’s Grant of the Faculty of Computer Science, Electronics and Telecommunications AGH UST.

References

  1. 1.
    Bogdanov, P. Mongiovì, M., Singh, A.K.: Mining heavy subgraphs in time-evolving networks. In: 2011 IEEE 11th International Conference on Data Mining, pp. 81–90 (2011)Google Scholar
  2. 2.
    Borge-Holthoefer, J., Baños, R.A., González-Bailón, S., Moreno, Y.: Cascading behaviour in complex socio-technical networks. J. Complex Netw. 1(1), 3–24 (2013)CrossRefGoogle Scholar
  3. 3.
    Gliwa, B., Koźlak, J., Zygmunt, A., Cetnarowicz, K.: Models of social groups in blogosphere based on information about comment addressees and sentiments. In: Aberer, K., Flache, A., Jager, W., Liu, L., Tang, J., Guéret, C. (eds.) SocInfo 2012. LNCS, vol. 7710, pp. 475–488. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-35386-4_35CrossRefGoogle Scholar
  4. 4.
    Harush, U., Barzel, B.: Dynamic patterns of information flow in complex networks. Nat. Commun. 8, 2181 (2017)CrossRefGoogle Scholar
  5. 5.
    Hui, C., Tyshchuk, Y., Wallace, W.A., Magdon-Ismail, M., Goldberg, M.: Information cascades in social media in response to a crisis: a preliminary model and a case study. In: Proceedings of the 21st International Conference on World Wide Web (WWW 2012 Companion), pp. 653–656. ACM, New York (2012)Google Scholar
  6. 6.
    Hulovatyy, H., Chen, T., Milenkovic, T.: Exploring the structure and function of temporal networks with dynamic graphlets. Bioinformatics 31 (2014).  https://doi.org/10.1093/bioinformatics/btv227CrossRefGoogle Scholar
  7. 7.
    Kabutoya, Y., Nishida, K., Fujimura, K.: Dynamic network motifs: evolutionary patterns of substructures in complex networks. In: Du, X., Fan, W., Wang, J., Peng, Z., Sharaf, Mohamed A. (eds.) APWeb 2011. LNCS, vol. 6612, pp. 321–326. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-20291-9_33CrossRefGoogle Scholar
  8. 8.
    Kovanen, L., Karsai, M., Kaski, K., Kertész, J., Saramäki, J.: IOP Publishing Lt, J. Stat. Mech. Theory Exp. 2011, November 2011Google Scholar
  9. 9.
    Sekara, V., Stopczynski, A., Lehmann, S.: Fundamental structures of dynamic social networks. PNAS 113, 9977–9982 (2016)CrossRefGoogle Scholar
  10. 10.
    Leskovec, J., McGlohon, M., Faloutsos, C., Glance, N., Hurst, M.: Patterns of cascading behavior in large blog graphs. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 551–556 (2007)CrossRefGoogle Scholar
  11. 11.
    Milardo, R., Johnson, M., Huston, T.: Developing close relationships: changing patterns of interaction between pair members and social networks. J. Pers. Soc. Psychol. 44(05), 964–976 (1983)CrossRefGoogle Scholar
  12. 12.
    Morse, S., Gonzalez, M., Markuzon, N.: Persistent cascades: measuring fundamental communication structure in social net-works. In: 2016 IEEE International Conference on Big Data, BigData 2016, Washington DC, USA, pp. 969–975, 5–8 December 2016Google Scholar
  13. 13.
    Oliwa, L., Kozlak, J.: Anomaly detection in dynamic social networks for identifying key events. In: 2017 International Conference on Behavioral, Economic, Socio-cultural Computing, BESC 2017, Krakow, Poland, 16–18 October 2017. IEEE (2017)Google Scholar
  14. 14.
    Pan, R.K., Saramäki, J.: Path lengths, correlations, and centrality in temporal networks. Phys. Rev. E 84, 016105 (2011)CrossRefGoogle Scholar
  15. 15.
    Rudek, K., Kozlak, J.: The influence of relationships strength on their duration in blogosphere. In: 2017 International Conference on Behavioral, Economic, Socio-cultural Computing, BESC 2017, Krakow, Poland, 16–18 October 2017. IEEE (2017)Google Scholar
  16. 16.
    Song, D., Wang, Y., Gao, X., Qu, S.X., Lai, Y.C., Wang, X.: Pattern formation and transition in complex networks, March 2017Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Computer Science, Electronics and TelecommunicationsAGH University of Science and TechnologyKrakówPoland

Personalised recommendations