Quantifying the Impact of Information Aggregation on Complex Networks: A Temporal Perspective

  • Fernando Mourão
  • Leonardo Rocha
  • Lucas Miranda
  • Virgílio Almeida
  • Wagner MeiraJr.
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5427)


Complex networks are a popular and frequent tool for modeling a variety of entities and their relationships. Understanding these relationships and selecting which data will be used in their analysis is key to a proper characterization. Most of the current approaches consider all available information for analysis, aggregating it over time. In this work, we studied the impact of such aggregation while characterizing complex networks. We model four real complex networks using an extended graph model that enables us to quantify the impact of the information aggregation over time. We conclude that data aggregation may distort the characteristics of the underlying real-world network and must be performed carefully.


Complex Network Degree Distribution Relationship Type Network Distance Information Aggregation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Newman, M.E.J.: The structure and function of complex networks. SIAM Review 45(2), 167–256 (2003)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Albert, R., Barabasi, A.L.: Statistical mechanics of complex networks. Reviews of Modern Physics 74, 47 (2002)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Albert, R., Jeong, H., Barabasi, A.L.: The diameter of the world wide web. Nature 401, 130 (1999)CrossRefGoogle Scholar
  4. 4.
    Elmacioglu, E., Lee, D.: On six degrees of separation in dblp-db and more. SIGMOD Rec. 34(2), 33–40 (2005)CrossRefGoogle Scholar
  5. 5.
    Dorogovtsev, S., Mendes, J.: Evolution of networks. Advances in Physics 51, 1079 (2002)CrossRefGoogle Scholar
  6. 6.
    Leskovec, J., Kleinberg, J., Faloutsos, C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proc. of the 11th ACM SIGKDD, pp. 177–187. ACM, New York (2005)Google Scholar
  7. 7.
    Wilson, E.O.: Consilience: The Unity of Knowledge. Knopf (1998)Google Scholar
  8. 8.
    Archdeacon, D.: Topological graph theory: A survey. Cong. Num. 115, 115–5 (1996)Google Scholar
  9. 9.
    Erdos, P., Renyi, A.: On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci 5, 17–61 (1960)MathSciNetMATHGoogle Scholar
  10. 10.
    Barabási, A.L., Bonabeau, E.: Scale-free networks. Scientific American 288, 60–69 (2003)CrossRefGoogle Scholar
  11. 11.
    Watts, D.J.: Small worlds: the dynamics of networks between order and randomness. Princeton University Press, Princeton (1999)MATHGoogle Scholar
  12. 12.
    Du, N., Wu, B., Pei, X., Wang, B., Xu, L.: Community detection in large-scale social networks. In: Proc. of the 9th WebKDD and 1st SNA-KDD, NY, USA, pp. 16–25. ACM, New York (2007)CrossRefGoogle Scholar
  13. 13.
    Said, Y.H., Wegman, E.J., Sharabati, W.K., Rigsby, J.T.: Social networks of author-coauthor relationships. Comput. Stat. Data Anal. 52(4), 2177–2184 (2008)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Barabasi, A.L., Jeong, H., Neda, Z., Ravasz, E., Schubert, A., Vicsek, T.: Evolution of the social network of scientific collaborations. PHYSICA A 311, 3 (2002)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Leskovec, J., Backstrom, L., Kumar, R., Tomkins, A.: Microscopic evolution of social networks. In: Proc. of the 11th ACM SIGKDD. ACM, New York (2008)Google Scholar
  16. 16.
    Kossinets, G., Kleinberg, J., Watts, D.: The structure of information pathways in a social communication network (June 2008)Google Scholar
  17. 17.
    Crandall, D., Cosley, D., Huttenlocher, D., Kleinberg, J., Suri, S.: Feedback effects between similarity and social influence in online communities. In: Proc. of ACM SIGKDD (2008)Google Scholar
  18. 18.
    Sharan, U., Neville, J.: Exploiting time-varying relationships in statistical relational models. In: Proc. of the 9th WebKDD and 1st SNA-KDD, pp. 9–15. ACM, New York (2007)CrossRefGoogle Scholar
  19. 19.
    Liben-Nowell, D., Kleinberg, J.: The Link-Prediction Problem for Social Networks. Journal-American Society for Information Science and Technology 58(7), 1019 (2007)CrossRefGoogle Scholar
  20. 20.
    Kossinets, G., Watts, D.: Empirical Analysis of an Evolving Social Network (2006)Google Scholar
  21. 21.
    Rocha, L., Mourao, F., Pereira, A., Gonçalves, M., Meira, W.: Exploiting temporal contexts in text classification. In: Proc. of ACM CIKM, Napa Valley, CA, USA. ACM, New York (2008)Google Scholar
  22. 22.
    Brieman, L., Spector, P.: Submodel selection and evaluation in regression: The x-random case. International Statistical Review 60, 291–319 (1992)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Fernando Mourão
    • 1
  • Leonardo Rocha
    • 1
  • Lucas Miranda
    • 1
  • Virgílio Almeida
    • 1
  • Wagner MeiraJr.
    • 1
  1. 1.Department of Computer ScienceFederal University of Minas GeraisBelo Horizonte MGBrazil

Personalised recommendations