Skip to main content
SpringerLink
Log in

Instant Messaging for Detecting Dynamic Ego-Centered Communities

  • Living reference work entry
  • First Online:
Book cover Encyclopedia of Social Network Analysis and Mining

  • 104 Accesses

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • Bródka P, Saganowski S, Kazienko P (2013) Ged: the method for group evolution discovery in social networks. Soc Netw Anal Min 3(1):1–14

    Article  MATH  Google Scholar 

  • Cazabet R, Amblard F (2014) Dynamic community detection. In: Encyclopedia of social network analysis and mining. Springer, New York, pp 404–414

    Google Scholar 

  • 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 554–560

    Chapter  Google Scholar 

  • Chan SY, Hui P, Xu K (2009) Community detection of time-varying mobile social networks. In: Complex sciences. Springer, Berlin, Heidelberg, pp 1154–1159

    Google Scholar 

  • Chen J, Za ıane O, Goebel R (2009) Local community identification in social networks. In: International conference on advances in social network analysis and mining, 2009. ASONAM’09. IEEE, pp 237–242

    Chapter  Google Scholar 

  • Clauset A (2005) Finding local community structure in networks. Phys Rev E 72(2):026,132

    Article  Google Scholar 

  • Eagle N, Pentland AS, Lazer D (2009) Inferring friendship network structure by using mobile phone data. Proc Natl Acad Sci 106(36):15,274–15,278

    Article  Google Scholar 

  • Ermentrout B (1998) Neural networks as spatio-temporal pattern-forming systems. Rep Prog Phys 61(4):353

    Article  Google Scholar 

  • Gao H, Tang J, Liu H (2012) Mobile location prediction in spatio-temporal context. In: Nokia mobile data challenge workshop 41:44

    Google Scholar 

  • 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 176–183

    Chapter  Google Scholar 

  • Hopcroft J, Khan O, Kulis B, Selman B (2004) Tracking evolving communities in large linked networks. Proc Natl Acad Sci 101(suppl 1):5249–5253

    Article  Google Scholar 

  • Lancichinetti A, Fortunato S, Kert´esz J (2009) Detecting the overlapping and hierarchical community structure in complex networks. New J Phys 11(3):033,015

    Google Scholar 

  • Li J, Huang L, Bai T, Wang Z, Chen H (2012) Cdbia: a dynamic community detection method based on incremental analysis. In: 2012 international conference on systems and informatics (ICSAI). IEEE, pp 2224–2228

    Chapter  Google Scholar 

  • Lu Z, Wen Y, Cao G (2013) Community detection in weighted networks: Algorithms and applications. In: 2013 I.E. international conference on pervasive computing and communications (PerCom). IEEE, pp 179–184

    Google Scholar 

  • Ngonmang B, Tchuente M, Viennet E (2012) Local community identification in social networks. Parallel Process Lett 22(01):1240,004

    Article  MathSciNet  MATH  Google Scholar 

  • Paevere P, Higgins A, Ren Z, Horn M, Grozev G, McNamara C (2014) Spatio-temporal modelling of electric vehicle charging demand and impacts on peak household electrical load. Sustain Sci 9(1):61–76

    Article  Google Scholar 

  • Rocha LE, Liljeros F, Holme P (2011) Simulated epidemics in an empirical spatiotemporal network of 50,185 sexual contacts. PLoS Comput Biol 7(3):e1001,109

    Article  Google Scholar 

  • 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:14072683

    Google Scholar 

  • Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905

    Article  Google Scholar 

  • Wang Y, Wu B, Du N (2008) Community evolution of social network: feature, algorithm and model. arXiv preprint arXiv:08044356

    Google Scholar 

  • Xie J, Szymanski BK (2012) Towards linear time overlapping community detection in social networks. In: Advances in knowledge discovery and data mining. Springer, Berlin, Heidelberg, pp 25–36

    Google Scholar 

  • Xu KS, Kliger M, Hero AO III (2011) Tracking communities in dynamic social networks. In: Social computing, behavioral-cultural modeling and prediction. Springer, Berlin, Heidelberg, pp 219–226

    Google Scholar 

  • Zeng X, Zhang Y (2013) Development of recurrent neural network considering temporal-spatial input dynamics for freeway travel time modeling. Comput Aided Civ Inf Eng 28(5):359–371

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, Cheikh Anta Diop University, Dakar–Fann, BP 5005, Senegal

    Ahmed Ould Mohamed Moctar & Idrissa Sarr

Authors
  1. Ahmed Ould Mohamed Moctar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Idrissa Sarr
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmed Ould Mohamed Moctar .

Editor information

Editors and Affiliations

  1. Computer Science, University of Calgary, Calgary, Alberta, Canada

    Reda Alhajj

  2. University of Calgary Computer Science, Calgary, Canada

    Jon Rokne

Section Editor information

  1. TOBB Economics and Technology University, Ankara, Turkey

    Tansel Ozyer

  2. Bilkent University, Ankara, Turkey

    Ozgur Ulusoy

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media LLC

About this entry

Cite this entry

Moctar, A.O.M., Sarr, I. (2017). Instant Messaging for Detecting Dynamic Ego-Centered Communities. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7163-9_110216-1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-7163-9_110216-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-7163-9

  • Online ISBN: 978-1-4614-7163-9

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

Publish with us

Policies and ethics

Search

Navigation