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Community Identification in Dynamic and Complex Networks

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  • First Online:
Encyclopedia of Social Network Analysis and Mining

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Synonyms

Community discovery; Dense subgraph; Social networks; Weblike networks

Community:

Locally dense subgraph in large globally sparse graph

Community Identification:

Extracting a community, which a given node belongs to

Power Law:

The frequency of an event varies as a power of the event's attribute

NP:

Nondeterministic polynomial time complexity

Definition

A community may be defined informally as a locally dense subgraph, of a significant size, in a large, globally sparse graph. Communities do not exist in the classical Erdos-Renyi random graph, but they do exist in graphs representing the Internet, the World Wide Web (WWW), and numerous social and biological systems. These graphs representing the real-world complex systems are large, dynamic, and random and are termed as complex networks. At least two different questions may be posed about the community structure in large graphs: (i) Given a graph, identify or extract all (i.e., sets of nodes that constitute) communities and (ii)...

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References

  • Agarwal N, Liu H, Tang L, Yu P (2012) Modeling blogger influence in a community. Soc Netw Anal Min 2: 139–162

    Google Scholar 

  • Alba RD (1973) A graph-theoretic definition of a sociometric clique. J Math Sociol 3:113–126

    MATH  MathSciNet  Google Scholar 

  • Albert R, Jeong H, Barabasi A (1999) Diameter of the world-wide web. Nature 401:130–131

    Google Scholar 

  • Caci B, Cardaci M, Tabacchi M (2011) Facebook as a small world: a topological hypothesis. Soc Netw Anal Min, 1–5

    Google Scholar 

  • Cami A, Deo N (2007) Techniques for analyzing dynamic random graph models of web-like networks: an overview. Networks 51:211–255

    MathSciNet  Google Scholar 

  • Cano P, Celma O, Koppenberger M, Buldú JM (2005) The topology of music recommendation networks. arXiv:physics/0512266v1

    Google Scholar 

  • Cano P, Koppenberger M, Wack N (2005) Content-based music audio recommendation. In: Proceedings of the 13th annual ACM international conference on Multimedia, Hilton. ACM, 1101181, pp 211–212

    Google Scholar 

  • Deo N (1974) Graph theory with applications to engineering and computer science. Prentice-Hall, Upper Saddle River

    MATH  Google Scholar 

  • Erdös P, Rényi A (1959) On random graphs. Publ Math Debr 6:290–297

    MATH  Google Scholar 

  • Flake GW, Lawrence S, Giles CL (2000) Efficient identification of web communities. In: Proceedings of the 6th ACM SIGKDD international conference on knowledge discovery and data mining, Boston. ACM, pp 150–160

    Google Scholar 

  • Fortunato S (2010) Community detection in graphs. Phys Rep 486:75–174

    MathSciNet  Google Scholar 

  • Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NP-completeness. Freeman, New York

    MATH  Google Scholar 

  • Goh K-I, Cusick ME, Valle D, Childs B, Vidal M, Barabási A-L (2007) The human disease network. Proc Natl Acad Sci 104:8685–8690

    Google Scholar 

  • Hu Y, Chen H, Zhang P, Li M, Di Z, Fan Y (2008) Comparative definition of community and corresponding identifying algorithm. Phys Rev E 78:026121

    Google Scholar 

  • Leskovec J, Singh A, Kleinberg J (2006) Patterns of influence in a recommendation network. Springer, Berlin/Heidelberg, pp 380–389

    Google Scholar 

  • Luccio F, Sami M (1969) On the decomposition of networks in minimally interconnected subnetworks. IEEE Trans Circuit Theory 16:184–188

    MathSciNet  Google Scholar 

  • Luce R, Perry A (1949) A method of matrix analysis of group structure. Psychometrika 14:95–116

    MathSciNet  Google Scholar 

  • Mokken RJ (1979) Cliques, clubs and clans. Qual Quant 13:161–173

    Google Scholar 

  • Newman MEJ (2001) The structure of scientific collaboration networks. Proc Natl Acad Sci USA 98:404–409

    MATH  MathSciNet  Google Scholar 

  • Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45:167–256

    MATH  MathSciNet  Google Scholar 

  • Papadopoulos S, Kompatsiaris Y, Vakali A, Spyridonos P (2012) Community detection in social media. Data Min Knowl Discov 24:515–554

    Google Scholar 

  • Radicchi F, Castellano C, Cecconi F, Loreto V, Parisi D (2004) Defining and identifying communities in networks. Proc Natl Acad Sci USA 101:2658–2663

    Google Scholar 

  • Rajkumar B, Mukkadim P, Athena V (2008) Content delivery networks. Springer, Berlin/Heidelberg

    Google Scholar 

  • Scanlon JM, Deo N (2008) Network communities based on maximizing average degree. Congressus Numerantium 190:183–192

    MATH  MathSciNet  Google Scholar 

  • Seidman SB (1983) Network structure and minimum degree. Soc Netw 5:269–287

    MathSciNet  Google Scholar 

  • Song Y, Zhuang Z, Li H, Zhao Q, Li J, Lee W-C, Giles CL (2008) Real-time automatic tag recommendation. In: Proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval, Singapore. ACM

    Google Scholar 

  • Sporns O (2011) The human connectome: a complex network. Ann N Y Acad Sci 1224:109–125

    Google Scholar 

  • Strogatz SH (2001) Exploring complex networks. Nature 410:268–276

    Google Scholar 

  • Vasudevan M, Balakrishnan H, Deo N (2009) Community discovery algorithms: an overview. Congressus Numerantium 196:127–142

    MATH  MathSciNet  Google Scholar 

  • Watts DJ, Strogatz SH (1998) Collective dynamics of small-world networks. Nature 393:440–442

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of EECS, University of Central Florida, Orlando, Orlando, FL, USA

    Mahadevan Vasudevan & Narsingh Deo

Authors
  1. Mahadevan Vasudevan
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  2. Narsingh Deo
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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

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Vasudevan, M., Deo, N. (2014). Community Identification in Dynamic and Complex Networks. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6170-8_380

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