Skip to main content
SpringerLink
Log in

Social Network Database Querying Based on Computing with Words

  • Chapter
  • First Online:
Flexible Approaches in Data, Information and Knowledge Management

Part of the book series: Studies in Computational Intelligence ((SCI,volume 497))

Abstract

Fuzzy relationships and their role in modeling weighted social relational networks are discussed. We describe how the idea of computing with words can provide a bridge between a network analyst’s linguistic description of social network concepts and the formal model of the network. We then turn to some examples of taking an analyst’s network concepts and formally representing them in terms of network properties. We first do this for the concept of clique and then for the idea of node importance. Finally we introduce the idea of vector–valued nodes and begin developing a technology of social network database theory.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

  1. Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, New York (1994)

    Book  Google Scholar 

  2. Scott, J.: Social Network Analysis. SAGE Publishers, Los Angeles (2000)

    Google Scholar 

  3. Newman, M.: Networks: An Introduction. Oxford University Press, New York (2010)

    Book  Google Scholar 

  4. Russell, M.A.: Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites. O’Reilly Media, Schasptopol, CA (2011)

    Google Scholar 

  5. Aggarwal, C.C.: Social Network Data Analytics. Springer, New York (2011)

    Book  MATH  Google Scholar 

  6. Prell, C.: Social Network Analysis: History, Theory and Methodology. Sage Publisher, London (2012)

    Google Scholar 

  7. Kadushin, C.: Understanding Social Networks: Theories, Concepts and Findings. Oxford University, New York (2012)

    Google Scholar 

  8. Yager, R.R.: Intelligent social network analysis using granular computing. Int. J. Intell. Syst. 23, 1196–1219 (2008)

    Google Scholar 

  9. Pivert, O., Bosc, P.: Fuzzy Preference Queries to Relational Databases. World Scientific, Singapore (2012)

    Book  MATH  Google Scholar 

  10. Zadeh, L.A.: Fuzzy logic = computing with words. IEEE Trans. Fuzzy Syst. 4, 103–111 (1996)

    Article  Google Scholar 

  11. Zadeh, L.A.: From computing with numbers to computing with words—from manipulation of measurements to manipulations of perceptions. IEEE Trans. Circuits Syst. 45, 105–119 (1999)

    Article  MathSciNet  Google Scholar 

  12. Zadeh, L.A.: Outline of a computational theory of perceptions based on computing with words. In: Sinha, N.K., Gupta, M.M. (eds.) Soft Computing and Intelligent Systems, pp. 3–22. Academic Press, Boston (1999)

    Google Scholar 

  13. Zadeh, L.A.: Generalized theory of uncertainty (GTU)-principal concepts and ideas. Comput. Stat. Data Anal. 51, 15–46 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  14. Zadeh, L.A.: Fuzzy logic. In: Meyers, A.R. (ed.) Encyclopedia of Complexity and Systems Science. Springer, Heidelberg (2009)

    Google Scholar 

  15. Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction. Kluwer Academic Publishers, Amsterdam (2003)

    Book  Google Scholar 

  16. Yager, R.R.: Human behavioral modeling using fuzzy and Dempster-Shafer theory. In: Liu, H., Salerno, J.J., Young, M.J. (eds.) Social Computing, Behavioral Modeling and Prediction, pp. 89–99. Springer, Berlin (2008)

    Chapter  Google Scholar 

  17. Rosenfeld, A.: Fuzzy graphs. In: Zadeh, L.A., Fu, K.S., Tanaka, K., Shimura, M. (eds.) Fuzzy Sets and their Applications to Cognitive and Decision Processes, pp. 77–97. Academic Press, New York (1975)

    Google Scholar 

  18. Delgado, M., Verdegay, J.L., Vila, M.A.: On the valuation and optimization problems in fuzzy graphs (a general approach and some particuar cases). ORSA J. Comput. 2, 74–83 (1990)

    Article  MATH  Google Scholar 

  19. Koczy, L.T.: Fuzzy graph in the evaluation and optimization of networks. Fuzzy Sets Syst. 46, 307–319 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  20. Yager, R.R.: Level sets and the extension principle for interval valued fuzzy sets and its application to uncertainty. Inf. Sci. 178, 3565–3576 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  21. Zadeh, L.A.: Similarity relations and fuzzy orderings. Inf. Sci. 3, 177–200 (1971)

    Article  MathSciNet  MATH  Google Scholar 

  22. Zadeh, L.: The concept of a linguistic variable and its application to approximate reasoning: Part 1. Inf. Sci. 8, 199–249 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  23. Murofushi, T., Sugeno, M.: Fuzzy measures and fuzzy integrals. In: Grabisch, M., Murofushi, T., Sugeno, M. (eds.) Fuzzy Measures and Integrals, pp. 3–41. Physica-Verlag, Heidelberg (2000)

    Google Scholar 

  24. Zadrozny, S., de Tré, G., de Caluwe, R., Kacprzyk, J.: An overview of fuzzy approaches to database querying. In: Galindo, J. (ed.) Handbook of Research on Fuzzy Information Processing in Databases, vol. 1, pp. 34–54. Information Science Reference, Hershey, PA (2008)

    Google Scholar 

  25. Yager, R.R.: Quantifier guided aggregation using OWA operators. Int. J. Intell. Syst. 11, 49–73 (1996)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Machine Intelligence Institute, Iona College, New Rochelle, NY, 10801, USA

    Ronald R. Yager

Authors
  1. Ronald R. Yager
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ronald R. Yager .

Editor information

Editors and Affiliations

  1. IRISA/ENSSAT, Université de Rennes 1, Lannion Cedex, France

    Olivier Pivert

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Sławomir Zadrożny

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Yager, R.R. (2014). Social Network Database Querying Based on Computing with Words. In: Pivert, O., Zadrożny, S. (eds) Flexible Approaches in Data, Information and Knowledge Management. Studies in Computational Intelligence, vol 497. Springer, Cham. https://doi.org/10.1007/978-3-319-00954-4_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-00954-4_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-00953-7

  • Online ISBN: 978-3-319-00954-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation