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Dynamic Community Detection

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  • First Online:
Encyclopedia of Social Network Analysis and Mining

Synonyms

Community detection; Dynamic communities; Evolution of communities; Evolving communities; Evolving networks; Temporal networks

Glossary

Community:

In this context, a community corresponds to a subgraph of a network, composed of nodes densely connected together and more sparsely connected to the rest of the network

Evolving network:

A network that changes along time. Nodes and edges can be added to or removed from the network. In weighted networks, weights can also evolve

Snapshot of a network:

A static network corresponding to all nodes and edges alive at a given time in an evolving network

Definition

Dynamic community detection is the process of finding relevant communities in a network that changes along time.

Introduction

Community detection is one of the most popular topics in the field of network analysis. Since the seminal paper of Girvan and Newman (2002), hundreds of papers have been published on the topic. From the initial problem of graph partitioning, in which each...

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References

  • Aynaud T, Guillaume JL (2010) Long range community detection. In: LAWDN- Latin-American workshop on dynamic networks, Buenos Aires

    Google Scholar 

  • Cazabet R, Amblard F (2011) Simulate to detect: a multi-agent system for community detection. In: IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (WI-IAT), vol 2. IEEE, Lyon, pp 402–408

    Google Scholar 

  • Cazabet R, Amblard F, Hanachi C (2010) Detection of overlapping communities in dynamical social networks. In: IEEE second international conference on social computing (SocialCom), Minneapolis, pp 309–314

    Google Scholar 

  • Cazabet R, Takeda H, Hamasaki M, Amblard F (2012) Using dynamic community detection to identify trends in user-generated content. Soc Netw Anal Min 2:361–371

    Article  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, Philadelphia, pp 554–560

    Google Scholar 

  • Chen W, Liu Z, Sun X, Wang Y (2010) A game-theoretic framework to identify overlapping communities in social networks. Data Min Knowl Disc 21(2):224–240

    Article  MathSciNet  Google Scholar 

  • Dhouioui Z, Akaichi J (2014) Tracking dynamic community evolution in social networks. In: IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM 10 Asonam (2014)), Beijing, pp 764–770

    Google Scholar 

  • Falkowski T, Barth A, Spiliopoulou M (2008) Studying community dynamics with an incremental graph mining algorithm. In: AMCIS 2008 proceedings, Toronto, p 29

    Google Scholar 

  • Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826

    Article  MathSciNet  MATH  Google Scholar 

  • Gorke RG, Maillard P, Schumm A, Staudt C, Wagner D (2013) Dynamic graph clustering combining modularity and smoothness. J Exp Algorithmics 18(1):1.1–1.29

    Article  MathSciNet  MATH  Google Scholar 

  • Greene D, Doyle D, Cunningham P (2010) Tracking the evolution of communities in dynamic social networks. In: International conference on advances in social networks analysis and mining (ASONAM). IEEE, Odense, pp 176–183

    Google Scholar 

  • Guo C, Wang J, Zhang Z (2014) Evolutionary community structure discovery in dynamic weighted networks. Phys A Stat Mech Appl 413:565–576

    Article  Google Scholar 

  • Holme P, Saramäki J (2012) Temporal networks. In: Physics reports. Elsevier, Amsterdam

    Google Scholar 

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

    Article  Google Scholar 

  • Ilhan N, Oguducu SG (2015) Predicting community evolution based on time series modeling. In DyNo workshop ASONAM, Paris

    Google Scholar 

  • Jdidia MB, Robardet C, Fleury E (2007) Communities detection and analysis of their dynamics in collaborative networks. In: Second international conference on digital information management, ICDIM’07, vol 2. Lyon, pp 744–749

    Google Scholar 

  • Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E 78(4):046110

    Article  Google Scholar 

  • Matias C, Rebafka T, and Villers F (2015) Estimation and clustering in a semiparametric Poisson process stochastic block model for longitudinal networks. arXiv preprint arXiv:1512.07075

    Google Scholar 

  • Mucha PJ, Richardson T, Macon K, Porter MA, Onnela JP (2010) Community structure in time-dependent, multiscale, and multiplex networks. Science 328(5980):876–878

    Article  MathSciNet  MATH  Google Scholar 

  • Palla G, Barabasi AL, Vicsek T (2007) Quantifying social group evolution. Nature 446(7136):664–667

    Article  Google Scholar 

  • Rossetti G, Pappalardo L, Pedreschi D, Giannotti F (2016) Tiles: an online algorithm for community discovery in dynamic social networks. Mach Learn:1–29. https://doi.org/10.1007/s10994-016-5582-8

  • Rosvall M, Bergstrom CT (2010) Mapping change in large networks. PLoS One 5(1):e8694

    Article  Google Scholar 

  • 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, San Jose, pp 717–726

    Google Scholar 

  • Viard J, Latapy M, Magnien C (2016) Computing maximal cliques in link streams. Theor Comput Sci 609:245–252

    Article  MathSciNet  MATH  Google Scholar 

  • Wang Y, Wu B, Du N (2008) Community evolution of social network: feature, algorithm and model. Sci Technol arXiv:0804.4356

    Google Scholar 

  • Xie J, Szymanski BK (2013) Labelrank: a stabilized label propagation algorithm for community detection in networks. In: Network Science Workshop (NSW), 2013 I.E. 2nd. IEEE, pp 138–143

    Google Scholar 

  • Yang T, Chi Y, Zhu S, Gong Y, Jin R (2011) Detecting communities and their evolutions in dynamic social networks? A bayesian approach. Mach Learn 82(2):157–189

    Article  MathSciNet  MATH  Google Scholar 

  • Zakrzewska Anita and Bader David A (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. ACM, Paris, pp 559–564

    Google Scholar 

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Author information

Authors and Affiliations

  1. LIP6, CNRS, Pierre and Marie Curie University, Paris, France

    Rémy Cazabet

  2. KDD Lab, University of Pisa, Pisa, Italy

    Giulio Rossetti

  3. IRIT – Université Toulouse 1 Capitole, 2, rue du Doyen Gabriel Marty, Toulouse, Cedex 9, France

    Frédéric Amblard

Authors
  1. Rémy Cazabet
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  2. Giulio Rossetti
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  3. Frédéric Amblard
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Corresponding author

Correspondence to Rémy Cazabet .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Reda Alhajj

  2. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Jon Rokne

Section Editor information

  1. TOBB Economics and Technology University, Ankara, Turkey

    Tansel Ozyer

  2. Bilkent University, Ankara, Turkey

    Ozgur Ulusoy

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Cazabet, R., Rossetti, G., Amblard, F. (2018). Dynamic Community Detection. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-7131-2_383

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