Advertisement

World Wide Web

, Volume 16, Issue 4, pp 421–447 | Cite as

Creation and growth of online social network

How do social networks evolve?
  • Katarzyna MusialEmail author
  • Marcin Budka
  • Krzysztof Juszczyszyn
Open Access
Article
First Online:

Abstract

Social networks are an example of complex systems consisting of nodes that can interact with each other and based on these activities the social relations are defined. The dynamics and evolution of social networks are very interesting but at the same time very challenging areas of research. In this paper the formation and growth of one of such structures extracted from data about human activities within online social networking system is investigated. Dynamics of both local and global characteristics are studied. Analysis of the dynamics of the network growth showed that it changes over time—from random process to power-law growth. The phase transition between those two is clearly visible. In general, node degree distribution can be described as the scale-free but it does not emerge straight from the beginning. Social networks are known to feature high clustering coefficient and friend-of-a-friend phenomenon. This research has revealed that in online social network, although the clustering coefficient grows over time, it is lower than expected. Also the friend-of-a-friend phenomenon is missing. On the other hand, the length of the shortest paths is small starting from the beginning of the network existence so the small-world phenomenon is present. The unique element of the presented study is that the data, from which the online social network was extracted, represents interactions between users from the beginning of the social networking site existence. The system, from which the data was obtained, enables users to interact using different communication channels and it gives additional opportunity to investigate multi-relational character of human relations.

Keywords

online social network complex system dynamics network growth and evolution dynamics of relationships of different types local and global network characteristics 
Download to read the full article text

References

  1. 1.
    Barabasi, A.L.: Linked: How Everything is Connected to Everything else and What it Means. Plume (2003)Google Scholar
  2. 2.
    Barrat, A., Barthelemy, M., Vespignani, A.: Dynamical Processes on Complex Networks. Cambridge University Press (2008)Google Scholar
  3. 3.
    Bisgin, H., Agarwal, N., Xu, X.: A study of homophily on social media. World Wide Web 15(2), 213–232 (2012)CrossRefGoogle Scholar
  4. 4.
    Bollobas, B.: Random Graphs. Academic, London (1985)zbMATHGoogle Scholar
  5. 5.
    Braha, D., Bar-Yam, Y.: From centrality to temporary fame: dynamic centrality in complex networks. Complexity 12, 59–63 (2006)CrossRefGoogle Scholar
  6. 6.
    Breiger, R.: The analysis of social networks. In: Handbook of Data Analysis, pp. 505–526. SAGE Publications (2004)Google Scholar
  7. 7.
    Bringmann, B., Berlingero, M., Bonch, F., Gionis, A.: Learning and predicting the evolution of social networks. IEEE Intell. Syst. 25(4), 26–35 (2010)CrossRefGoogle Scholar
  8. 8.
    Carrington, P., Scott, J., Wasserman, S.: Social networks. In: Models and Methods in Social Network Analysis. Cambridge University Press, Cambridge (2005)CrossRefGoogle Scholar
  9. 9.
    Fredericks, K., Durlan, M.: The historical evolution and basic concepts of social network analysis. New Dir. Eval. 2005(107), 15–23 (2006)CrossRefGoogle Scholar
  10. 10.
    Hill, S., Braha, D.: Dynamic model of time-dependent complex networks. Phys. Rev. E 82, 046105 (2010). arXiv:0901.4407v2 CrossRefGoogle Scholar
  11. 11.
    Holland, J.: Hidden Order: How Adaptation Builds Complexity. Basic Books (1996)Google Scholar
  12. 12.
    Kazienko, P., Musial, K., Kajdanowicz, T.: Multidimensional social network and its application to the social recommender system. IEEE Trans. Syst. Man Cybern., Part A, Syst. Humans 41(4), 746–759 (2011)CrossRefGoogle Scholar
  13. 13.
    Kazienko, P., Musial, K., Brodka, P., Skibicki, K.: Analysis of neighbourhoods in multi-layered social networks. J. Comput. Intell. Syst. 5(3), 582–596 (2012). doi: 10.1080/18756891.2012.696922 CrossRefGoogle Scholar
  14. 14.
    Kumar, R., Novak, J., Tomkins, A.: Microscopic evolution of social network. In: The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press (2006)Google Scholar
  15. 15.
    Lescovec, J., Backstrom, L., Kumar, R., Tomkins, A.: Microscopic evolution of social networks. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2008)Google Scholar
  16. 16.
    Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)CrossRefGoogle Scholar
  17. 17.
    Musial, K., Kazienko, P.: Social networks on the internet. World Wide Web J. 1–42 (2012). doi: 10.1007/s11280-011-0155-z, online first
  18. 18.
    Musial, K., Sastry, N.: Social media—are they underpinned by social or interest-based interactions? In: 4th Annual Workshop on Simplifying Complex Networks for Practitioners (SIMPLEX2012) Co-Located with World Wide Web Conference (WWW2012) (2012)Google Scholar
  19. 19.
    Newman, M.E.J.: Assortative mixing in networks. Phys. Rev. E 89(20), 208701 (2002)Google Scholar
  20. 20.
    Newman, M., Park, J.: Why social networks are different from other types of networks. Phys. Rev. E 68(3), 036122 (2003)CrossRefGoogle Scholar
  21. 21.
    Shen, H.T., Hua, X.S., Luo, J., Oria, V.: Guest editorial: content, concept and context mining in social media. World Wide Web 15(2), 115–116 (2012)CrossRefGoogle Scholar
  22. 22.
    Time.com: One minute on Facebook—person of the year 2010—Time (2011)Google Scholar
  23. 23.
    Wasserman, S., Faust, K.: Social Network Analysis Methods and Applications. Cambridge University Press, New York (1994)CrossRefGoogle Scholar
  24. 24.
    Watts, D.: Small Worlds Dynamic of Networks between Order and Randomness. Princeton University Press (2002)Google Scholar
  25. 25.
    Watts, D., Strogatz, S.: Collective dynamics of small-world networks. Nature 393(6684), 440–444 (1998)CrossRefGoogle Scholar
  26. 26.
    Yao, J., Cui, B., Huang, Y., Zhou, Y.: Bursty event detection from collaborative tags. World Wide Web 15(2), 171–195 (2012)CrossRefGoogle Scholar
  27. 27.
    Zhong, N., Liu, J., Yao, Y.Y.: In search of the Wisdom Web. IEEE Computer 35(11), 27–31 (2002)CrossRefGoogle Scholar
  28. 28.
    Zhong, N., Liu, J., Yao, Y.Y.: Web Intelligence (WI). In: The Encyclopedia of Computer Science and Engineering, vol. 5, pp. 3062–3072. Wiley (2009)Google Scholar
  29. 29.
    Zhong, N., Ma, J.H., Huang, R.H., Liu, J.M., Yao, Y.Y., Zhang, Y.X., Chen, J.H.: Research challenges and perspectives on Wisdom Web of Things (W2T). J. Supercomputing (Springer) (2010). doi: 10.1007/s11227-010-0518-8 Google Scholar

Copyright information

© The Author(s) 2012

Authors and Affiliations

  • Katarzyna Musial
    • 1
    Email author
  • Marcin Budka
    • 2
  • Krzysztof Juszczyszyn
    • 3
  1. 1.Department of Informatics, School of Mathematical and Natural SciencesKing’s College LondonLondonUK
  2. 2.Smart Technology Research CentreBournemouth UniversityPooleUK
  3. 3.Faculty of Computer Science and ManagementWroclaw University of TechnologyWroclawPoland

Personalised recommendations