Peer-to-Peer Networking and Applications

, Volume 10, Issue 6, pp 1272–1284 | Cite as

Events detection and community partition based on probabilistic snapshot for evolutionary social network

  • Zhongnan ZhangEmail author
  • Lei Hu
  • Ming Qiu
  • Fangyuan Gao


Most of the existing researches simply convert associations of nodes within the snapshot of the evolutionary social network to the weight of edges. However, because of the obvious Matthew effect existing in the interactions of nodes in the real social network, the association strength matrices extracted directly by snapshots are extremely uneven. This paper introduces a new evolutionary social network model. Firstly, we generate probabilistic snapshots of the evolutionary social network data. Afterwards, we use the probabilistic factor model to detect the variation points brought by network events. Finally we partition the network community based on snapshots with stable structures before and after the variation points. According to experimental results, our proposed probabilistic snapshot model is effective for network events detection and network community partition.


Evolutionary social network Probabilistic snapshot Events detection Community partition 



This research is supported by the Science and Technology Program of Xiamen, China (No.3502Z20153026); Key Program of Science and Technology of Fujian, China (No. 2014H0044, 2015H0037); NSFC (No. 61402387); NSFC (No. 61402390).


  1. 1.
    Gilbert E, Karahalios K (2009) Predicting tie strength with social media[C]. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '09). ACM, New York, NY, USA, pp 211–220Google Scholar
  2. 2.
    Granovetter M (1973) The strength of weak ties[J]. Am J Sociol 78(6):lCrossRefGoogle Scholar
  3. 3.
    Barabási A-L, Bonabeau E (2003) Scale-free networks. Scientific American. vol. 288, no 5, pp 50–59Google Scholar
  4. 4.
    Scott J, Carrington PJ (2011) The SAGE handbook of social network analysis[M]. SAGE publicationsGoogle Scholar
  5. 5.
    Heiby EM (1995) Chaos theory, nonlinear dynamical models, and psychological assessment[J]. Psychol Assess 7(1):5CrossRefGoogle Scholar
  6. 6.
    Tang L, Liu H (2010) Community detection and mining in social media[J]. Synth Lect Data Min Knowl Disc 2(1):21MathSciNetGoogle Scholar
  7. 7.
    Leskovec J, Horvitz E (2008) Planetary-scale views on a large instant-messaging network. In: Proceedings of the 17th international conference on World Wide Web (WWW '08). ACM, New York, NY, USA, pp 915–924Google Scholar
  8. 8.
    Jackson MO, Wolinsky A (1996) A strategic model of social and economic networks[J]. J Econ Theory 71(1):44–74MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Onnela JP, Saramäki J, Hyvönen J et al (2007) Analysis of a large-scale weighted network of one-to-one human communication[J]. New J Phys 9(6):179CrossRefGoogle Scholar
  10. 10.
    Easley D, Kleinberg J (2010) Networks, crowds, and markets[J]. Cambridge Univ Press 6(1):6.1zbMATHGoogle Scholar
  11. 11.
    Weigend AS, Huberman BA, Rumelhart DE (1990) Predicting the future: a connectionist approach[J]. Int J Neural Syst 1(03):193–209CrossRefGoogle Scholar
  12. 12.
    Kossinets G, Kleinberg J, Watts D (2008) The structure of information pathways in a social communication network. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '08). ACM, New York, NY, USA, pp 435–443Google Scholar
  13. 13.
    Goyal A, Bonchi F, Lakshmanan LVS (2010) Learning influence probabilities in social networks. In: Proceedings of the third ACM international conference on Web search and data mining (WSDM '10). ACM, New York, NY, USA, pp 241–250Google Scholar
  14. 14.
    Mitsudomi T, Morita S, Yatabe Y et al (2010) Gefitinib versus cisplatin plus docetaxel in patients with non-small-cell lung cancer harbouring mutations of the epidermal growth factor receptor (WJTOG3405): an open label, randomised phase 3 trial[J]. Lancet Oncol 11(2):121–128CrossRefGoogle Scholar
  15. 15.
    Zhao-nian Z, Jian-zhong L, Gao H, Shuo Z (2009) Mining frequent subgraph patterns from uncertain graphs. J Softw 20(11):2965–2976CrossRefGoogle Scholar
  16. 16.
    Salakhutdinov R, Mnih A (2007) Probabilistic matrix factorization[C]//21st Annual Conference on Neural Information Processing Systems, NIPS 2007. Vancouver, BC, Canada, pp 1257–1264Google Scholar
  17. 17.
    Hosmer DW Jr., Lemeshow S (2013) Applied logistic regression. John Wiley & Sons, Inc., Hoboken, New JerseyGoogle Scholar
  18. 18.
    Guan N, Wei L, Luo Z, et al (2013) Limited-memory fast gradient descent method for graph regularized nonnegative matrix factorization. PLoS ONE, 8(10), e77162Google Scholar
  19. 19.
    Rhrissorrakrai K, Gunsalus KC (2011) MINE: module identification in networks[J]. BMC Bioinfo 12(1):192CrossRefGoogle Scholar
  20. 20.
    Abello J, Buchsbaum AL, Westbrook JR (1998) A functional approach to external graph algorithms[M]. Algorithms—ESA’98. Springer, Berlin Heidelberg, pp 332–343zbMATHGoogle Scholar
  21. 21.
    Capocci A, Servedio VDP, Caldarelli G et al (2004) Communities detection in large networks[M]. Algorithms and Models for the Web-Graph. Springer, Berlin Heidelberg, pp 181–187CrossRefzbMATHGoogle Scholar
  22. 22.
    Borgatti SP, Everett MG, Freeman LC (2002) Ucinet for Windows: Software for social network analysis. Harvard, MA: Analytic technologiesGoogle Scholar
  23. 23.
    McCarney R, Warner J, Iliffe S et al (2007) The Hawthorne effect: a randomised, controlled trial[J]. BMC Med Res Methodol 7(1):30CrossRefGoogle Scholar
  24. 24.
    Kollios G, Potamias M, Terzi E (2013) Clustering large probabilistic graphs[J]. IEEE Trans Knowl Data Eng 25(2):325–336CrossRefGoogle Scholar
  25. 25.
    Shamir R, Sharan R, Tsur D (2004) Cluster graph modification problems[J]. Discret Appl Math 144(1):173–182MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Nir A, Noa A-E, Edo L, van Zuylen A (2012) Improved approximation algorithms for bipartite correlation clustering. SIAM J Comput 41(5):1110–1121MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Freire M, Plaisant C, Shneiderman B, et al (2010) ManyNets: an interface for multiple network analysis and visualization. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '10). ACM, New York, NY, USA, pp 213–222Google Scholar
  28. 28.
    Keila PS, Skillicorn DB (2005) Structure in the Enron email dataset[J]. Comput Math Organ Theory 11(3):183–199CrossRefzbMATHGoogle Scholar
  29. 29.
    Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks[J]. Phys Rev E 69(2):026113CrossRefGoogle Scholar
  30. 30.
    Shen Y (2013) Detect local communities in networks with an outside rate coefficient[J]. Phys A Stat Mech Appl 392(12):2821–2829MathSciNetCrossRefGoogle Scholar
  31. 31.
    Hollander M, Wolfe DA, Chicken E (2013) Nonparametric statistical methods. John Wiley & Sons, Inc., Hoboken, New JerseyGoogle Scholar
  32. 32.
    Zhao YY, Qin B, Liu T (2010) Sentiment analysis[J]. J Softw 21(8):1834–1848CrossRefGoogle Scholar
  33. 33.
    Jojic O, Shukla M, Bhosarekar N (2011) A probabilistic definition of item similarity. In: Proceedings of the fifth ACM conference on Recommender systems (RecSys '11). ACM, New York, NY, USA, pp 229–236Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Zhongnan Zhang
    • 1
    Email author
  • Lei Hu
    • 1
  • Ming Qiu
    • 1
  • Fangyuan Gao
    • 1
  1. 1.Software SchoolXiamen UniversityXiamenChina

Personalised recommendations