Knowledge and Information Systems

, Volume 53, Issue 1, pp 109–151 | Cite as

DANCer: dynamic attributed networks with community structure generation

  • C. LargeronEmail author
  • P. N. Mougel
  • O. Benyahia
  • O. R. Zaïane
Regular Paper


Most networks, such as those generated from social media, tend to evolve gradually with frequent changes in the activity and the interactions of their participants. Furthermore, the communities inside the network can grow, shrink, merge, or split, and the entities can move from one community to another. The aim of community detection methods is precisely to detect the evolution of these communities. However, evaluating these algorithms requires tests on real or artificial networks with verifiable ground truth. Dynamic networks generators have been recently proposed for this task, but most of them consider only the structure of the network, disregarding the characteristics of the nodes. In this paper, we propose a new generator for dynamic attributed networks with community structure that follow the properties of real-world networks. The evolution of the network is performed using two kinds of operations: Micro-operations are applied on the edges and vertices, while macro-operations on the communities. Moreover, the properties of real-world networks such as preferential attachment or homophily are preserved during the evolution of the network, as confirmed by our experiments.


Social network Graph generator Community structure 


  1. 1.
    Akoglu L, Faloutsos C (2009) RTG: a recursive realistic graph generator using random typing. Data Min Knowl Discov 19(2):194–209MathSciNetCrossRefGoogle Scholar
  2. 2.
    Akoglu L et al (2008) RTM: laws and a recursive generator for weighted time-evolving graphs. In: Eighth IEEE international conference on data mining, 2008 (ICDM’08). IEEE, pp 701–706Google Scholar
  3. 3.
    Albert R, Barabási A-L (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47–97MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Amaral LAN et al (2000) Classes of small-world networks. Proc Natl Acad Sci 97(21):11149–11152CrossRefGoogle Scholar
  5. 5.
    Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Benson AR et al (2014) Learning multifractal structure in large networks. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 1326–1335Google Scholar
  7. 7.
    Chung F, Lu L (2002) The average distances in random graphs with given expected degrees. Proc Natl Acad Sci 99(25):15879–15882MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Dang TA (2012) Analysis of communities in social networks. Ph.D. thesis, Université Paris 13Google Scholar
  9. 9.
    Easley D, Kleinberg J (2010) Networks, crowds and markets: reasoning about a highly connected world. Cambridge University Press, CambridgeCrossRefzbMATHGoogle Scholar
  10. 10.
    Erdős P, Rényi A (1960) On the evolution of random graphs. Publ Math Inst Hung Acad Sci 5:17–61MathSciNetzbMATHGoogle Scholar
  11. 11.
    Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Gong NZ et al (2012) Evolution of social-attribute networks: measurements, modeling, and implications using Google+. In: ACM conference on internet measurement conference (IMC). ACM, pp 131–144Google Scholar
  13. 13.
    Görke R et al (2012) An efficient generator for clustered dynamic random networks. Springer, BerlinCrossRefzbMATHGoogle Scholar
  14. 14.
    Görke R, Staudt C (2009) A generator for dynamic clustered random graphs. Tech. rep., ITI Wagner, Department of Informatics, Universität Karlsruhe. Informatik, Uni Karlsruhe, TR 2009-7Google Scholar
  15. 15.
    Granell C et al (2015) A benchmark model to assess community structure in evolving networks. CoRR arXiv:1501.05808
  16. 16.
    Holland PW, Leinhardt S (1971) Transitivity in structural models of small groups. Comp Group Stud 2:107–124CrossRefGoogle Scholar
  17. 17.
    Kim M, Leskovec J (2012) Multiplicative attribute graph model of real-world networks. Internet Math 8(1–2):113–160MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Lancichinetti A, Fortunato S (2009) Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Phys Rev E 80(1):016118CrossRefGoogle Scholar
  19. 19.
    Lancichinetti A et al (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E 78(4):046110CrossRefGoogle Scholar
  20. 20.
    Largeron C et al (2015) Generating attributed networks with communities. PLoS ONE 10(4):e0122777CrossRefGoogle Scholar
  21. 21.
    Lazarsfeld PF, Merton RK (1954) Friendship as a social process: a substantive and methodological analysis. Freedom Control Mod Soc 18(1):18–66Google Scholar
  22. 22.
    Leskovec J et al (2008) Microscopic evolution of social networks. In: ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 462–470Google Scholar
  23. 23.
    Leskovec J et al (2005a) Realistic, mathematically tractable graph generation and evolution, using kronecker multiplication. In: Knowledge discovery in databases: PKDD 2005. Springer, Berlin, pp 133–145Google Scholar
  24. 24.
    Leskovec J et al (2010) Kronecker graphs: an approach to modeling networks. J Mach Learn Res 11:985–1042MathSciNetzbMATHGoogle Scholar
  25. 25.
    Leskovec J et al (2005b) Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. ACM, pp 177–187Google Scholar
  26. 26.
    McPherson M et al (2001) Birds of a feather: homophily in social networks. Annu Rev Sociol 27(1):415–444CrossRefGoogle Scholar
  27. 27.
    Milgram S (1967) The small-world problem. Psychol Today 2:60–67Google Scholar
  28. 28.
    Newman ME (2006) Finding community structure in networks using the eigenvectors of matrices. Phys Rev E 74(3):036104MathSciNetCrossRefGoogle Scholar
  29. 29.
    Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026113CrossRefGoogle Scholar
  30. 30.
    Palla G et al (2010) Multifractal network generator. Proc Natl Acad Sci 107(17):7640–7645CrossRefGoogle Scholar
  31. 31.
    Pfeiffer JJ III et al (2014) Attributed graph models: modeling network structure with correlated attributes. In: Proceedings of the 23rd international conference on World Wide Web. ACM, pp 831–842Google Scholar
  32. 32.
    Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440–442CrossRefGoogle Scholar
  33. 33.
    Wong LH et al (2006) A spatial model for social networks. Phys A Stat Mech Its Appl 360(1):99–120MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  1. 1.Univ Lyon, UJM-Saint-Etienne, CNRSInstitut d’Optique Graduate choolSAINT-ETIENNEFrance
  2. 2.Department of Computer ScienceUniversity of AlbertaEdmontonCanada

Personalised recommendations