Relation Between Facebook Stories and Hours of a Day

  • Hradesh KumarEmail author
  • Sanjeev Kumar Yadav
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 4)


In recent development of computer technology, social networks are evolved as complex networks. Most challenging questions are to understand dynamics of user behavior on social network applications. In this paper, structural and dynamical modeling issues have been investigated. Social networks are treated as random graphs where a node is indicator variable of an entity on social network. The term random graph refers to the messy nature of the arrangement of links between different nodes. ER random graphs are generated by linking pair of randomly selected nodes. There are several characteristics of nodes to categorize them such as average path length, clustering coefficient to the each node. Nodes categorized with the help of self-organizing map algorithm and other statistical inference mechanism. Activities on social network are such as posting, commenting, sharing, and sending message, watch videos, and advertisements which are modeled as random events on random graphs.


Facebook Social network User activities Random post 


  1. 1.
    Bocaletti, S., Latora, V., Moreno, Y., Chavez, M. and Hwang, D., U, 2006, Complex networks: Structure and dynamics. Elsevier, Physics Reports 424, pp 175–308.Google Scholar
  2. 2.
    Clark, J., W., 2012, Correlating a person to a person, ASE/IEEE International conference on social computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust, pp 851–859.Google Scholar
  3. 3.
    Eftekhar, A., Fullwood, C. and Morris, N., 2014, Capturing personality from Facebook photos and photo related activities: how much exposure do you need, Elsevier, Computers in Human Behavior, pp 162–170.Google Scholar
  4. 4.
    Farahbakhsh, R., Han, X., Cuevas, A. and Crespi, N., 2013, Analysis of publicly disclosed information in Facebook profiles, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp 699–705.Google Scholar
  5. 5.
    Guo, Q., Zhou, T., Liu, J., G., Bai, W., J., Wang, B., H. and Zhao, M., 2006, Growing scale-free small networks with tunable assortative coefficient, Elsevier, Physica A 371, pp 814–822.Google Scholar
  6. 6.
    Handayani, P., W. and Lisdianingrum, W., 2011, Impact Analysis on Free Online Marketing Using Socila Network Facebook: Case Study SMEs in Indonesia, ICACSIS, pp 171–176.Google Scholar
  7. 7.
    Hayes, M., Cooke, K., V., S. and Muench, F., 2015, Understanding Facebook use and the psychological affects of use across generations, Elsevier, Computers in Human Behavior 49, pp 507–511.Google Scholar
  8. 8.
    Hirsch, A., O. and Sunder, S., S., 2014, Posting, commenting, tagging: Effects of sharing news stories on Facebook, Elsevier, Computers in Human Behavior 44, pp 240–249.Google Scholar
  9. 9.
    Hollenbaugh, E., E. and Ferris, A., L., 2015, Prediction of honesty, intent and valence of Facebook self-disclosure, Elsevier, Computers in Human Behavior 50, pp 4565–464.Google Scholar
  10. 10.
    Hradesh, K., Sanjeev, Yadav, 2015, Investigating Social Network as Complex Network and Dynamics of User Activities, IJCA, Vol. 125, No. 7, pp 13–18.Google Scholar
  11. 11.
    Jiang, M., Cui, P., Wang, F., Zhu, W. and Yang, S., 2014, Scalable Recommendation with Social Contextual Information, IEEE Transactions on Knowledge and Data Engineering, Vol. 26, November 2014, pp 2789–2802.Google Scholar
  12. 12.
    Khadangi, E., Zarean, A., Bagheri, A. and Jafrabadi, A., B., 2013, Measuring Relationship Strength in Online Social Networks based on users’ activities and profile information, 3rd International Conference on Computer and Knowledge Engineering (ICCKE 2013), Ferdowsl University of Mashhad.Google Scholar
  13. 13.
    Khil, M., Larsson, R., Arvidsson, A. and Aurelius, A., 2014, Analysis of Facebook content demand patterns, IEEE.Google Scholar
  14. 14.
    Kirman, B., Lawson, S. and Linehan, C., 2009, Gaming on and off the Social Graph: The Social Structure of Facebook Games, International Conference on Computational Science and Engineering, IEEE, pp 627–632.Google Scholar
  15. 15.
    Kumar, H., Yadav, S., 2016, Surveying SNA Tools: How far & How Close to the Researcher, IJEAST, Vol. 1, Issue 6, pp 176–187.Google Scholar
  16. 16.
    Mahanti, A., Carlsson, N., Mahanti, A., Arlitt, M. and Williamson, C., 2013, A Tale of Tails: Power Laws in Internet Measurements, IEEE, pp 59–64.Google Scholar
  17. 17.
    Muangngeon, A. and Erjongmanee, S., 2015, Analysis of Facebook Activity Usage through Network and Human Perspectives, IEEE, pp 13–18.Google Scholar
  18. 18.
    Naim, E., B., Krapivsky, P., L. and Render, S., 2004, Extremel Properties of Random Structures, Springer-Verlag Berlin, pp 211–233.Google Scholar
  19. 19.
    Nguyen, K. and Tran, D., A., 2011, An Analysis of Activities in Facebook, The 8th Annual IEEE Consumer Communications and Networking Conference – Emerging and Innovative Consumer Technologies and Applications, pp 388–392.Google Scholar
  20. 20.
    Quinn, D., Liming, C. and Mulvenna, M., 2011, Does Age Make a Difference In The Behavior Of Online Social Network Users?, IEEE International Conference on Internet of Things and Cyber, Physical and Social Computing, pp 266–272.Google Scholar
  21. 21.
    Rybnicek, M., Poisel, R. and Tjoa, S., 2013, Facebook Watchdog: A Research Agenda For Detecting Online Grooming and Bullying Activities, IEEE International conference on Systems, Man and Cybernatics, pp 2854–2859.Google Scholar
  22. 22.
    Salamanos, N., Voudigari, E., Papageorgiou, T. and Vazirgiannis, M., 2012, Discovering Correlation between Communities and Likes in Facebook, IEEE International Conference on Green Computing and Communications, Conference on Internet of Things and Conference on Cyber, Physical and Social Computing, pp 368–371.Google Scholar
  23. 23.

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringKIET Group of InstitutionsGhaziabadIndia

Personalised recommendations