Skip to main content
SpringerLink
Log in

Combining Link and Content for Community Detection

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

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 1,500.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 549.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Airoldi EM, Blei DM, Fienberg SE, Xing EP (2006) Mixed membership stochastic block models for relational data with application to protein-protein interactions. In: Proceedings of the international biometrics society annual meeting, Montréal, Québec, Canada

    Google Scholar 

  • Baumes J, Goldberg M, Krishnamoorty M, Magdon-Ismail M (2005a) Finding communities by clustering a graph into overlapping subgraphs. In: Proceedings of the 2nd IADIS applied computing, Algarve

    Google Scholar 

  • Baumes J, Goldberg M, Magdon-Ismail M (2005b) Efficient identification of overlapping communities. In: Proceedings of the 3rd IEEE international conference on intelligence and security informatics, Atlanta

    Google Scholar 

  • Blei DM, Lafferty JD (2006) Correlated topic models. In: Proceedings of the 23rd international conference on machine learning, Pittsburgh

    Google Scholar 

  • Blei DM, Ng AY, Jordan MI, Lafferty J (2003) Latent dirichlet allocation. J Mach Learn Res 3: 993–1022

    MATH  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, KDD'06, Philadelphia, pp 554–560

    Google Scholar 

  • Chi Y, Song X, Zhou D, Hino K, Tseng BL (2009) On evolutionary spectral clustering. ACM Trans Knowl Discov Data 3:17:1–17:30

    Google Scholar 

  • Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70:066111

    Google Scholar 

  • Cohn D, Chang H (2000) Learning to probabilistically identify authoritative documents. In: Proceedings of the 17th international conference on machine learning, Stanford

    Google Scholar 

  • Cohn D, Hofmann T (2001) The missing link – a probabilistic model of document content and hypertext connectivity. In: Proceedings of the 13th advanced in neural information processing systems, Vancouver, British Columbia, Canada

    Google Scholar 

  • Deerwester S, Dumais ST, Furnas GW, Landauer TK, Harshman R (1990) Indexing by latent semantic analysis. J Am Soc Inf Sci 41:391–407

    Google Scholar 

  • Defays D (1977) An efficient algorithm for a complete link method. Comput J 20:364–366

    MATH  MathSciNet  Google Scholar 

  • Erosheva E, Fienberg S, Lafferty J (2004) Mixed membership models of scientific publications. Proc Natl Acad Sci 101:5220–5227

    Google Scholar 

  • Gregory S (2007) An algorithm to find overlapping community structure in networks. In: Proceedings of the 11th European conference on principles and practice of knowledge discovery in databases, Warsaw

    Google Scholar 

  • Gruber A, Rosen-Zvi M, Weiss Y (2007) Hidden topic markov models. In: Proceedings of the 11th artificial intelligence and statistics, San Juan, Puerto Rico

    Google Scholar 

  • Gruber A, Rosen-Zvi M, Weiss Y (2008) Latent topic models for hypertext. In: Proceedings of the 24th annual conference on uncertainty in artificial intelligence, Helsinki

    Google Scholar 

  • Hoff PD, Raftery AE, H MS (2002) Latent space approaches to social network analysis. J Am Stat Assoc 97:10–90

    Google Scholar 

  • Hofman JM, Wiggins CH (2008) A Bayesian approach to network modularity. Phys Rev Lett 100:258701

    Google Scholar 

  • Hofmann T (1999) Probabilistic latent semantic indexing. In: Proceedings of 15th uncertainty in artificial intelligence, Stockholm

    Google Scholar 

  • Holland PW, Leinhardt S (1974) The statistical analysis of local structure in social networks. Technical report

    Google Scholar 

  • Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice-Hall, Englewood Cliffs

    MATH  Google Scholar 

  • Kemp C, Griffiths TL, Tenenbaum JB (2004) Discovering latent classes in relational data. Technical report, MIT

    Google Scholar 

  • Kolmogorov V, Zabih R (2004) What energy functions can be minimized via graph cuts. IEEE Trans Pattern Anal Mach Intell 26:147–159

    Google Scholar 

  • Lin YR, Chi Y, Zhu S, Sundaram H, Tseng BL (2008) Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. In: Proceedings of the 17th international conference on world wide web, WWW'08, Beijing, pp 685–694

    Google Scholar 

  • Nallapati RM, Ahmed A, Xing EP, Cohen WW (2008) Joint latent topic models for text and citations. In: Proceeding of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, Las Vegas

    Google Scholar 

  • Newman MEJ (2006) Finding community structure in networks using the eigenvectors of matrices. Phys Rev E 74:36–104

    Google Scholar 

  • Newman MEJ, Girvan M (2003) Finding and evaluating community structure in networks. Phys Rev E 69: 26–113

    Google Scholar 

  • Pinney JW, Westhead DR (2006) Betweenness-based decomposition methods for social and biological networks. In: Proceedings of the 25th interdisciplinary statistics and bioinformatics, Leeds, UK

    Google Scholar 

  • Ren W, Yan G, Liao X, Cheng Y (2007) A simple probabilistic algorithm for detecting community structure in social networks. Phys Rev E 79:36–111

    Google Scholar 

  • Rosen-Zvi M, Griffiths T, Steyvers M, Smyth P (2004) The author-topic model for authors and documents. In: Proceedings of the 20th conference on uncertainty in artificial intelligence, Banff

    Google Scholar 

  • Sibson R (1973) SLINK: an optimally efficient algorithm for the single-link cluster method. Comput J 16: 30–34

    MathSciNet  Google Scholar 

  • Wang X, Mohanty N, McCallum A (2005) Group and topic discovery from relations and their attributes. In: Proceedings of the 18th advances in neural information processing systems, Vancouver

    Google Scholar 

  • Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, Cambridge/New York

    Google Scholar 

  • Xu W, Liu X, Gong Y (2003) Document clustering based on non-negative matrix factorization. In: Proceedings of the 26th annual international ACM SIGIR conference on research and development in informaion retrieval, Toronto

    Google Scholar 

  • Yang T, Chi Y, Zhu S, Gong Y, Jin R (2009a) A Bayesian approach toward finding communities and their evolutions in dynamic social networks. In: Proceedings of the 9th SIAM international conference on data mining, Sparks

    Google Scholar 

  • Yang T, Jin R, Chi Y, Zhu S (2009b) Combining link and content for community detection: a discriminative approach. In: Proceedings of the 15th ACM SIGKDD conference on knowledge discovery and data mining, Paris, pp 927–936

    Google Scholar 

  • Yang T, Chi Y, Zhu S, Gong Y, Jin R (2010) Directed network community detection: a popularity and productivity link model. In: Proceedings of the 10th SIAM international conference on data mining, Columbus, pp 742–753

    Google Scholar 

  • Yu K, Yu S, Tresp V (2005) Soft clustering on graphs. In: Proceedings of 18th advances in neural information processing systems, Vancouver

    Google Scholar 

  • Zhu S, Yu K, Chi Y, Gong Y (2007) Combining content and link for classification using matrix factorization. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, Amsterdam

    Google Scholar 

Recommended Reading

  • Ding CHQ, He X, Zha H, Gu M, Simon HD (2001) A min-max cut algorithm for graph partitioning and data clustering. In: Proceedings of 1st IEEE international conference on data mining, Chicago

    Google Scholar 

  • Fu W, Song L, Xing EP (2009) Dynamic mixed membership blockmodel for evolving networks. In: Proceedings of the 26th annual international conference on machine learning, Montreal, pp 329–336

    Google Scholar 

  • Hagen L, Kahng AB (1992) New spectral methods for ratio cut partitioning and clustering. IEEE Trans Comput-Aided Des Integr Circuits Syst 11:1074–1085 http://en.wikipedia.org/wiki/Community_structure

  • Newman MEJ (2003) Fast algorithm for detecting community structure in networks. Phys Rev E 69

    Google Scholar 

  • Newman MEJ (2006b) Modularity and community structure in networks. Proc Natl Acad Sci 103:8577–8582

    Google Scholar 

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

    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 82:157–189

    MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Machine Learning Lab, GE Global Research, San Ramon, CA, USA

    Tianbao Yang

  2. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Rong Jin

  3. NEC Laboratories America, Inc., Cupertino, CA, USA

    Yun Chi & Shenghuo Zhu

Authors
  1. Tianbao Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rong Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yun Chi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shenghuo Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this entry

Cite this entry

Yang, T., Jin, R., Chi, Y., Zhu, S. (2014). Combining Link and Content for Community Detection. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6170-8_214

Download citation

Publish with us

Policies and ethics

Search

Navigation