Tracking local communities in streaming graphs with a dynamic algorithm

  • Anita ZakrzewskaEmail author
  • David A. Bader
Original Article


A variety of massive datasets, such as social networks and biological data, are represented as graphs that reveal underlying connections, trends, and anomalies. Community detection is the task of discovering dense groups of vertices in a graph. Its one specific form is seed set expansion, which finds the best local community for a given set of seed vertices. Greedy, agglomerative algorithms, which are commonly used in seed set expansion, have been previously designed only for a static, unchanging graph. However, in many applications, new data are constantly produced, and vertices and edges are inserted and removed from a graph. We present an algorithm for dynamic seed set expansion, which maintains a local community over time by incrementally updating as the underlying graph changes. We show that our dynamic algorithm outputs high-quality communities that are similar to those found when using a standard static algorithm. It works well both when beginning with an already existing graph and in the fully streaming case when starting with no data. The dynamic approach is also faster than re-computation when low latency updates are needed.


Batch Size Community Detection Static Algorithm Dynamic Algorithm Fitness Score 
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.



The work depicted in this paper was partially sponsored by Defense Advanced Research Projects Agency (DARPA) under agreement #HR0011-13-2-0001 (DARPA PERFECT). The content, views and conclusions presented in this document do not necessarily reflect the position or the policy of DARPA or the U.S. Government, no official endorsement should be inferred.


  1. Aktunc R, Toroslu IH, Ozer M, Davulcu H (2015) A dynamic modularity based community detection algorithm for large-scale networks: DSLM. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015. ACM, pp 1177–1183Google Scholar
  2. Andersen R, Chung F, Lang K (2006) Local graph partitioning using pagerank vectors. In: 47th Annual IEEE symposium on foundations of computer science, 2006. (FOCS’06). IEEE, pp 475–486Google Scholar
  3. Andersen R, Lang KJ (2006) Communities from seed sets. In: Proceedings of the 15th international conference on World Wide Web. ACM, pp 223–232Google Scholar
  4. Asur S, Parthasarathy S, Ucar D (2009) An event-based framework for characterizing the evolutionary behavior of interaction graphs. ACM Trans Knowl Discov Data (TKDD) 3(4):16Google Scholar
  5. Aynaud T, Fleury E, Guillaume JL, Wang Q (2013) Communities in evolving networks: definitions, detection, and analysis techniques. In: Mukherjee A, Choudhury M, Peruani F, Ganguly N, Mitra B (eds) Dynamics on and of complex networks, vol. 2. Springer, New York, pp 159–200Google Scholar
  6. Aynaud T, Guillaume JL (2010) Static community detection algorithms for evolving networks. In: WiOpt’10: modeling and optimization in mobile, Ad Hoc, and wireless networks. IEEE, pp 508–514Google Scholar
  7. Bagrow JP, Bollt EM (2005) Local method for detecting communities. Phys Rev E 72(4):046–108CrossRefGoogle Scholar
  8. Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech: Theory Exp 10:P10008CrossRefGoogle Scholar
  9. Cazabet R, Amblard F (2014) Encyclopedia of social network analysis and mining, chapter dynamic community detection. Springer, New York, pp 404–414Google Scholar
  10. Chakrabarti D, Kumar R, Tomkins A (2006) Evolutionary clustering. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 554–560Google Scholar
  11. Chen J, Zaiane OR, Goebel R (2009) Detecting communities in large networks by iterative local expansion. In: International conference on computational aspects of social networks, 2009. (CASON’09). IEEE, pp 105–112Google Scholar
  12. Chung FR (1997) Spectral graph theory, vol 92. American Mathematical Society, ProvidencezbMATHGoogle Scholar
  13. Clauset A (2005) Finding local community structure in networks. Phys Rev E 72(2):026–132CrossRefGoogle Scholar
  14. Derényi I, Palla G, Vicsek T (2005) Clique percolation in random networks. Phys Rev Lett 94(16):160–202CrossRefzbMATHGoogle Scholar
  15. Dinh TN, Xuan Y, Thai MT (2009) Towards social-aware routing in dynamic communication networks. In: 2009 IEEE 28th International on performance computing and communications conference (IPCCC). IEEE, pp 161–168Google Scholar
  16. Evans T, Lambiotte R (2010) Line graphs of weighted networks for overlapping communities. Eur Phys J B 77(2):265–272CrossRefGoogle Scholar
  17. Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174MathSciNetCrossRefGoogle Scholar
  18. Greene D, Doyle D, Cunningham P (2010) Tracking the evolution of communities in dynamic social networks. In: 2010 international conference on advances in social networks analysis and mining (ASONAM). IEEE, pp 176–183Google Scholar
  19. Havemann F, Heinz M, Struck A, Gläser J (2011) Identification of overlapping communities and their hierarchy by locally calculating community-changing resolution levels. J Stat Mech: Theory Exp 1:P01023Google Scholar
  20. Hopcroft J, Khan O, Kulis B, Selman B (2004) Tracking evolving communities in large linked networks. Proc Natl Acad Sci 101(suppl 1):5249–5253CrossRefGoogle Scholar
  21. Jdidia MB, Robardet C, Fleury E (2007) Communities detection and analysis of their dynamics in collaborative networks. In: ICDIM, pp 744–749Google Scholar
  22. Lancichinetti A, Fortunato S, Kertész J (2009) Detecting the overlapping and hierarchical community structure in complex networks. New J Phys 11(3):033,015CrossRefGoogle Scholar
  23. Lancichinetti A, Radicchi F, Ramasco JJ, Fortunato S (2011) Finding statistically significant communities in networks. PLoS One 6(4):e18,961CrossRefGoogle Scholar
  24. Lee C, Reid F, McDaid A, Hurley N (2010) Detecting highly overlapping community structure by greedy clique expansion. In: 4th SNA-KDD workshop, p 3342Google Scholar
  25. Lin YR, Chi Y, Zhu S, Sundaram H, Tseng BL (2009) Analyzing communities and their evolutions in dynamic social networks. ACM Trans Knowl Discov Data (TKDD) 3(2):8Google Scholar
  26. Mucha PJ, Richardson T, Macon K, Porter MA, Onnela JP (2010) Community structure in time-dependent, multiscale, and multiplex networks. Science 328(5980):876–878MathSciNetCrossRefzbMATHGoogle Scholar
  27. Newman ME, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026–113CrossRefGoogle Scholar
  28. Ning H, Xu W, Chi Y, Gong Y, Huang TS (2010) Incremental spectral clustering by efficiently updating the eigen-system. Pattern Recognit 43(1):113–127CrossRefzbMATHGoogle Scholar
  29. Palla G, Barabási AL, Vicsek T (2007) Quantifying social group evolution. Nature 446(7136):664–667CrossRefGoogle Scholar
  30. Plantié M, Crampes M (2013) Survey on social community detection. In: Ramzan N, van Zwol R, Lee J-S, Clüver K, Hua X-S (eds) Social media retrieval. Springer, London, pp 65–85Google Scholar
  31. Riedy J, Bader DA (2013) Multithreaded community monitoring for massive streaming graph data. In: 2013 IEEE 27th international parallel and distributed processing symposium workshops and PhD Forum (IPDPSW). IEEE, pp 1646–1655Google Scholar
  32. Riedy J, Bader DA, Jiang K, Pande P, Sharma R (2011) Detecting communities from given seeds in social networks. Technical Report GT-CSE-11-01, Georgia Institute of Technology.
  33. Shang J, Liu L, Xie F, Chen Z, Miao J, Fang X, Wu C (2014) A real-time detecting algorithm for tracking community structure of dynamic networks. arXiv preprint arXiv:1407.2683
  34. Spiliopoulou M (2011) Evolution in social networks: a survey. In: Aggarwal CC (ed) Social network data analytics. Springer, pp 149–175Google Scholar
  35. Takaffoli M, Rabbany R, Zaïane OR (2013) Incremental local community identification in dynamic social networks. In: Proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and mining. ACM, pp 90–94Google Scholar
  36. Tang L, Liu H (2010) Community detection and mining in social media. Synth Lect Data Min Knowl Discov 2(1):1–137CrossRefGoogle Scholar
  37. Tantipathananandh C, Berger-Wolf T, Kempe D (2007) A framework for community identification in dynamic social networks. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 717–726Google Scholar
  38. The koblenz network collection KONECT (2015).
  39. Waltman L, van Eck NJ (2013) A smart local moving algorithm for large-scale modularity-based community detection. The Eur Phys J B 86(11):1–14CrossRefGoogle Scholar
  40. Xie J, Kelley S, Szymanski BK (2013) Overlapping community detection in networks: the state-of-the-art and comparative study. ACM Comput Surv (CSUR) 45(4):43CrossRefzbMATHGoogle Scholar
  41. Xie J, Szymanski BK (2012)Towards linear time overlapping community detection in social networks. In: Advances in knowledge discovery and data mining. Springer, pp 25–36Google Scholar
  42. Zakrzewska A, Bader DA (2015) A dynamic algorithm for local community detection in graphs. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015, (ASONAM 15). ACM, New York, pp 559–564Google Scholar

Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  1. 1.Computational Science and EngineeringGeorgia Institute of TechnologyAtlantaUSA

Personalised recommendations