Skip to main content

Role Sets and Division of Work at Two Levels of Collective Agency: The Case of Blockmodeling a Multilevel (Inter-individual and Inter-organizational) Network

  • Chapter
  • First Online:
Book cover Multilevel Network Analysis for the Social Sciences

Part of the book series: Methodos Series ((METH,volume 12))

Abstract

The chapter presents a blockmodeling analysis of multilevel (inter-individual and inter-organizational) networks. Several approaches are presented and used to blockmodel such networks. Each blockmodel represents a system of roles (White HC, Boorman SA, Breiger RL, Am J Sociol 81:730–780, 1976) and therefore a form of division of work that is likely to change over time in fields of organized collective action. Using a case study, we show that while the systems of roles are quite similar at both levels (core-periphery-like structures with bridging cores interpreted in terms of division of work between actors’ and organizations’ specialties, location, status, etc.), the roles are performed by units with different characteristics at different levels. The added value of this true multilevel analysis is to show how groups at different levels are connected. In the empirical case analyzed in the chapter, the division of work at the level of individuals and the division of work at the level of laboratories can complement and strengthen each other for some segments of the population, while this reinforcement does not work for other segments. For the same roles, the mix of specialties at one level is different from the mix of specialties at the other level, notably because the two levels do not manage the same resources. Thus, this analysis tracks the meeting of top-down and bottom-up pressures towards structural alignment between levels.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
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

Notes

  1. 1.

    For a more general version of the described approaches see Žiberna (2014).

  2. 2.

    See Žiberna (2014) (including footnotes) for needed modifications in the case of other types of two-mode networks.

  3. 3.

    Since single-relational networks are represented by matrices, the multi-relational networks are represented by multiway matrices as used by Borgatti and Everett (1992).

  4. 4.

    We do not imply that firms can (never) access resources through employees’ (personal) networks.

  5. 5.

    We could use different levels as a “base” level, that is the level to which other levels are reshaped. The partitions obtained using different base levels might then slightly differ when reshaped to the same level, especially if in the two-mode network units from both sets of nodes can have many ties (to nodes of the other set).

  6. 6.

    Which is usually treated as a multirelational network.

  7. 7.

    The same network was analyzed using a pre-specified approach assuming cohesive groups structure within each level in the example section of the paper presenting blockmodeling of multilevel networks (Žiberna 2014).

  8. 8.

    We thank David Lazega for creating this variable.

  9. 9.

    The Fish/Pond status also takes into account the size of the laboratories (in addition to the multilevel network).

  10. 10.

    With perhaps less stringent demands in terms of densities. For related issues, see Lazega et al. (2011).

  11. 11.

    Conversion of the network of researchers to the laboratories’ “space” is also possible, although more complex.

  12. 12.

    Even these similarities would be deemed very low by Steinley (2004); however he used these indices with a different purpose.

  13. 13.

    We use activity to refer to the outgoing ties, so the more active units are those having more outgoing ties.

  14. 14.

    We use “recognized” since the direction of ties on the laboratories level is more an indication of who recognized the tie than of the pure direction of ties (“actions”).

References

  • Anderson, C. J., Wasserman, S., & Faust, K. (1992). Building stochastic blockmodels. Social Networks, 14, 137–161.

    Article  Google Scholar 

  • Arabie, P., Boorman, S. A., & Levitt, P. R. (1978). Constructing blockmodels: How and why. Journal of Mathematical Psychology, 17, 21–63.

    Article  Google Scholar 

  • Batagelj, V., Doreian, P., & Ferligoj, A. (1992a). An optimizational approach to regular equivalence. Social Networks, 14, 121–135.

    Article  Google Scholar 

  • Batagelj, V., Ferligoj, A., & Doreian, P. (1992b). Direct and indirect methods for structural equivalence. Social Networks, 14, 63–90.

    Article  Google Scholar 

  • Batagelj, V., Ferligoj, A., & Doreian, P. (1998). Fitting pre-specified blockmodels. In C. Hayashi, K. Yajima, H. H. Bock, N. Ohsumi, Y. Tanaka, & Y. Baba (Eds.), Data science, classification, and related methods (pp. 199–206). Tokyo: Springer.

    Chapter  Google Scholar 

  • Batagelj, V., Mrvar, A., Ferligoj, A., & Doreian, P. (2004). Generalized blockmodeling with Pajek. Metodološki Zvezki, 1, 455–467.

    Google Scholar 

  • Bellotti, E. (2012). Getting funded. Multi-level network of physicists in Italy. Social Networks, 34, 215–229.

    Article  Google Scholar 

  • Boorman, S. A., & White, H. C. (1976). Social structure from multiple networks. II. Role structures. American Journal of Sociology, 81, 1384–1446.

    Article  Google Scholar 

  • Borgatti, S. P., & Everett, M. G. (1992). Regular blockmodels of multiway, multimode matrices. Social Networks, 14, 91–120.

    Article  Google Scholar 

  • Brass, D. J., Galaskiewicz, J., Greve, H. R., & Tsai, W. P. (2004). Taking stock of networks and organizations: A multilevel perspective. Academy of Management Journal, 47, 795–817.

    Article  Google Scholar 

  • Breiger, R. (1974). The duality of persons and groups. Social Forces, 53, 181–190.

    Article  Google Scholar 

  • Breiger, R., Boorman, S., & Arabie, P. (1975). Algorithm for clustering relational data with applications to social network analysis and comparison with multidimensional-scaling. Journal of Mathematical Psychology, 12, 328–383.

    Article  Google Scholar 

  • Brusco, M., & Steinley, D. (2011). A tabu-search heuristic for deterministic two-mode blockmodeling of binary network matrices. Psychometrika, 76, 612–633.

    Article  Google Scholar 

  • Brusco, M., Doreian, P., Steinley, D., & Satornino, C. B. (2013). Multiobjective blockmodeling for social network analysis. Psychometrika, 78, 498–525.

    Article  Google Scholar 

  • Burt, R. (1976). Positions in networks. Social Forces, 55, 93–122.

    Article  Google Scholar 

  • Doreian, P., Batagelj, V., & Ferligoj, A. (1994). Partitioning networks based on generalized concepts of equivalence. The Journal of Mathematical Sociology, 19, 1–27.

    Article  Google Scholar 

  • Doreian, P., Batagelj, V., & Ferligoj, A. (2004). Generalized blockmodeling of two-mode network data. Social Networks, 26, 29–53.

    Article  Google Scholar 

  • Doreian, P., Batagelj, V., & Ferligoj, A. (2005a). Generalized blockmodeling. New York: Cambridge University Press.

    Google Scholar 

  • Doreian, P., Batagelj, V., & Ferligoj, A. (2005b). Generalized blockmodeling. Cambridge: Cambridge University Press.

    Google Scholar 

  • Holland, P. W., Laskey, K. B., & Leinhardt, S. (1983). Stochastic blockmodels: First steps. Social Networks, 5, 109–137.

    Article  Google Scholar 

  • Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2, 193–218.

    Article  Google Scholar 

  • Iacobucci, D., & Wasserman, S. (1990). Social networks with 2 sets of actors. Psychometrika, 55, 707–720.

    Article  Google Scholar 

  • Kronegger, L., Ferligoj, A., & Doreian, P. (2011). On the dynamics of national scientific systems. Quality and Quantity, 45, 989–1015.

    Article  Google Scholar 

  • Lazega, E., & Mounier, L. (2002). Interdependent entrepreneurs and the social discipline of their cooperation: A research programme for structural economic sociology in a society of organizations. In O. Favereau & E. Lazega (Eds.), Conventions and structures in economic organization (pp. 147–199). Cheltenham: Edward Elgar.

    Google Scholar 

  • Lazega, E., Mounier, L., Stofer, R., & Tripier, A. (2004). Discipline scientifique et discipline sociale: Réseaux de conseil, apprentissage collectif et innovation dans la recherche française sur le cancer (1997–1999). Recherches Sociologiques, 35, 3–28.

    Google Scholar 

  • Lazega, E., Mounier, L., Jourda, M.-T., & Stofer, R. (2006). Organizational vs. personal social capital in scientists’ performance: A multi-level network study of elite French cancer researchers (1996–1998). Scientometrics, 67, 27–44.

    Article  Google Scholar 

  • Lazega, E., Jourda, M.-T., Mounier, L., & Stofer, R. (2008). Catching up with big fish in the big pond? Multi-level network analysis through linked design. Social Networks, 30, 159–176.

    Article  Google Scholar 

  • Lazega, E., Sapulete, S., & Mounier, L. (2011). Structural stability regardless of membership turnover? The added value of blockmodelling in the analysis of network evolution. Quality and Quantity, 45, 129–144.

    Article  Google Scholar 

  • Lazega, E., Jourda, M.-T., & Mounier, L. (2013). Network lift from dual alters: Extended opportunity structures from a multilevel and structural perspective. European Sociological Review, 29, 1226–1238.

    Article  Google Scholar 

  • Mali, F., Kronegger, L., Doreian, P., & Ferligoj, A. (2012). Dynamic scientific co-authorship networks. In A. Scharnhorst, K. Börner, & P. Besselaar (Eds.), Models of science dynamics, understanding complex systems (pp. 195–232). Berlin: Springer.

    Google Scholar 

  • Multilevel Network Modeling Group. (2012). What are multilevel networks. University of Manchester. Available at: http://mnmg.co.uk/Multilevel%20Networks.pdf

  • R Core Team. (2013). R: A language and environment for statistical computing. Vienna, Austria. Available at http://www.R-project.org/

  • Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66, 846–850.

    Article  Google Scholar 

  • Snijders, T. A. B., & Nowicki, K. (1997). Estimation and prediction for stochastic blockmodels for graphs with latent block structure. Journal of Classification, 14, 75–100.

    Article  Google Scholar 

  • Snijders, T. A. B., Lomi, A., & Torló, V. J. (2013). A model for the multiplex dynamics of two-mode and one-mode networks, with an application to employment preference, friendship, and advice. Social Networks, 35, 265–276.

    Article  Google Scholar 

  • Steinley, D. (2004). Properties of the Hubert-Arable adjusted rand index. Psychological Methods, 9, 386–396.

    Article  Google Scholar 

  • Wang, P., Robins, G., Pattison, P., & Lazega, E. (2013). Exponential random graph models for multilevel networks. Social Networks, 35, 96–115.

    Article  Google Scholar 

  • Wasserman, S., & Iacobucci, D. (1991). Statistical modeling of one-mode and 2-mode networks – Simultaneous analysis of graphs and bipartite graphs. British Journal of Mathematical and Statistical Psychology, 44, 13–43.

    Article  Google Scholar 

  • White, H. C., Boorman, S. A., & Breiger, R. L. (1976). Social structure from multiple networks. I. Blockmodels of roles and positions. American Journal of Sociology, 81, 730–780.

    Article  Google Scholar 

  • Žiberna, A. (2007). Generalized blockmodeling of valued networks. Social Networks, 29, 105–126.

    Article  Google Scholar 

  •  Žiberna, A. (2013a). Generalized blockmodeling of sparse networks. Metodološki Zvezki, 10, 99–119.

    Google Scholar 

  • Žiberna, A. (2013b). Blockmodeling 0.2.2: An R package for generalized and classical blockmodeling of valued networks. Available at: http://www2.arnes.si/~aziber4/blockmodeling/

  • Žiberna, A. (2014). Blockmodeling of multilevel networks. Social Networks, 39, 46–61.

    Article  Google Scholar 

Download references

Acknowledgements

Section “Multilevel Blockmodeling” and pars of section “Analysis of a Multilevel Network of Cancer Researchers in France” are a modified version of parts from Žiberna (2014, pp. 48–51). Reprinted from Social Networks, Vol 39, Aleš Žiberna, “Blockmodeling of Multilevel Networks,” 46–61, Copyright (2014), with permission from Elsevier.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Aleš Žiberna .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Žiberna, A., Lazega, E. (2016). Role Sets and Division of Work at Two Levels of Collective Agency: The Case of Blockmodeling a Multilevel (Inter-individual and Inter-organizational) Network. In: Lazega, E., Snijders, T. (eds) Multilevel Network Analysis for the Social Sciences. Methodos Series, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-319-24520-1_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-24520-1_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24518-8

  • Online ISBN: 978-3-319-24520-1

  • eBook Packages: Social SciencesSocial Sciences (R0)

Publish with us

Policies and ethics