Skip to main content
SpringerLink
Log in

On the use of external information in social network analysis

  • Regular Article
  • Published:
Advances in Data Analysis and Classification Aims and scope Submit manuscript

Abstract

Network analysis focuses on links among interacting units (actors). Interactions are often derived from the presence of actors at events or activities (two-mode network) and this information is coded and arranged in a typical affiliation matrix. In addition to the relational data, interest may focus on external information gathered on both actors and events. Our aim is to explore the effect of external information on the formation of ties by setting a strategy able to decompose the original affiliation matrix by linear combinations of data vectors representing external information with a suitable matrix of coefficients. This allows to obtain peculiar relational data matrices that include the effect of external information. The derived adjacency matrices can then be analyzed from the network analysis perspective. In particular, we look for groups of structurally equivalent actors obtained through clustering methods. Illustrative examples and a real dataset in the framework of scientific collaboration will give a major insight into the proposed strategy.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Aluja Banet T, Lebart L (1985) Factorial analysis upon a graph. Bull Tech Centre Statist Inform Appl 3: 4–34

    Google Scholar 

  • Benali H, Escofier B (1990) Analyse factorielle lissée et analyse factorielle des diffèrences locales. Rev Statist Appl 38: 55–76

    Google Scholar 

  • Burt RS (1976) Positions in networks. Social Forces 55: 93–122

    Article  MathSciNet  Google Scholar 

  • Butts CT (2010) Software Manual for the R sna Package. R package version 2.1

  • Davis A, Burleigh B, Gardner M, Gardner R (1941) Deep south: a social anthropological study of caste and class. University of Chicago Press, Chicago

    Google Scholar 

  • De Stefano D, Vitale MP (2009) Exploring the pattern of scientific collaboration networks. In: Ingrassia S, Rocci R (eds) Book of short papers classification and data analysis. CLEUP, Padova, pp 161–164

    Google Scholar 

  • Ferligoj A, Batagelj V (1982) Clustering with relational constraint. Psychometrika 47: 413–426

    Article  MathSciNet  MATH  Google Scholar 

  • Giordano G, Vitale MP (2007) Factorial contiguity maps to explore relational data patterns. Statist Appl 19: 297–306

    Google Scholar 

  • Glanzel W, Schubert A (2005) Analyzing scientific networks through co-authorship. In: Moed H, Glanzel W, Schmoch U (eds) Handbook of quantitative science and technology research. Springer, Netherlands, pp 257–276

    Chapter  Google Scholar 

  • Katz JS, Martin BR (1997) What is research collaboration?. Res Policy 26: 1–18

    Article  Google Scholar 

  • Lazarsfeld P, Merton RK (1954) Friendship as a social process: a substantive and methodological analysis. In: Berger M, Abel T, Page CH (eds) Freedom and control in modern society. Van Nostrand, New York, pp 18–66

    Google Scholar 

  • Lebart L (1969) Analyse statistique de la contiguité. Annales de l’ISUP: publications de l’Institut de statistique de l’Université de Paris. ISUP, Paris pp 81–112

  • Lorrain F, White HC (1971) Structural equivalence of individuals in social networks. J Math Sociol 1: 49–80

    Article  Google Scholar 

  • McPherson M, Smith-Lovin L, Cook J (2001) Birds of a feather: homophily in social networks. Ann Rev Sociol 27: 415–444

    Article  Google Scholar 

  • Melin G, Persson O (1996) Studying research collaboration using co-authorships. Scientometrics 36: 363–377

    Article  Google Scholar 

  • Robins GL, Pattison P, Kalish Y, Lusher D (2007) An introduction to exponential random graph (p*) models for social networks. Social Netw 29: 173–191

    Article  Google Scholar 

  • Rogers EM (2003) Diffusion of innovations, 5th edn. Free Press, New York

    Google Scholar 

  • Searle SR (1997) Linear models. Wiley, NewYork

    MATH  Google Scholar 

  • Suits DB (1957) Use of dummy variables in regression equations. JASA 52(280): 548–551

    MATH  Google Scholar 

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

    Google Scholar 

  • White H, Boorman S, Breiger R (1976) Social structure from multiple networks: I. Blockmodels of roles and positions. Am J Sociol 81: 730–780

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Salerno, Via Ponte Don Melillo, 84084, Fisciano (SA), Italy

    Giuseppe Giordano & Maria Prosperina Vitale

Authors
  1. Giuseppe Giordano
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maria Prosperina Vitale
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giuseppe Giordano.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Giordano, G., Vitale, M.P. On the use of external information in social network analysis. Adv Data Anal Classif 5, 95–112 (2011). https://doi.org/10.1007/s11634-010-0080-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11634-010-0080-5

Keywords

Mathematics Subject Classification (2000)

Search

Navigation