Skip to main content
SpringerLink
Log in
Book cover

Social Network Analysis - Community Detection and Evolution pp 27–43Cite as

Hierarchical and Matrix Structures in a Large Organizational Email Network: Visualization and Modeling Approaches

  • Chapter
  • First Online:

Part of the book series: Lecture Notes in Social Networks ((LNSN))

Abstract

This paper presents findings from a study of the email network of a large scientific research organization, focusing on methods for visualizing and modeling organizational hierarchies within large, complex network datasets. In the first part of the paper, we find that visualization and interpretation of complex organizational network data is facilitated by integration of network data with information on formal organizational divisions and levels. By aggregating and visualizing email traffic between organizational units at various levels, we derive several insights into how large subdivisions of the organization interact with each other and with outside organizations. Our analysis shows that line and program management interactions in this organization systematically deviate from the idealized pattern of interaction prescribed by “matrix management”. In the second part of the paper, we propose a power law model for predicting degree distribution of organizational email traffic based on hierarchical relationships between managers and employees. This model considers the influence of global email announcements sent from managers to all employees under their supervision, and the role support staff play in generating email traffic, acting as agents for managers. We also analyze patterns in email traffic volume over the course of a work week.

This is a preview of subscription content, 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

Learn about institutional subscriptions

Notes

  1. 1.

    This contribution is an extended version of [32].

References

  1. Allen TJ, Cohen SI (1969) Information flow in research and development laboratories. Adm Sci Q 14(1):12–19

    Article  Google Scholar 

  2. Barabasi A-L, Ravasz E, Vicsek T (2001) Deterministic scale-free networks. Phys A 299:559–564

    Google Scholar 

  3. Bugos GE (1993) Programming the American aerospace industry, 1954–1964: the business structures of technical transactions. Bus Econ Hist 22:210–222

    Google Scholar 

  4. Burt RS (1992) Structural holes: the social structure of competition. Harvard University Press, Cambridge

    Google Scholar 

  5. Chapanond A, Krishnamoorthy MS, Yener B (2005) Graph theoretic and spectral analysis of Enron email data. Comput Math Organ Theory 11:265–281

    Google Scholar 

  6. Clauset A, Shalizi CR, Newman MEJ (2009) Power-law distributions in empirical data. SIAM Rev 51:661–703

    Article  MATH  MathSciNet  Google Scholar 

  7. Collins HM (1985) Changing order: replication and induction in scientific practice. Sage, London

    Google Scholar 

  8. Diesner J, Frantz TL, Carley KM (2005) Communication networks from the Enron email corpus ‘It’s always about the people. Enron is no different’. Comput Math Organ Theory 11:201–228

    Google Scholar 

  9. Doreian P, Batagelj V, Ferligoj A (2005) Generalized blockmodeling. Cambridge University Press, Cambridge

    Google Scholar 

  10. Freeman LC (2009) Methods of social network visualization. In: Meyers RA (ed) Encyclopedia of complexity and systems science. Springer, Berlin, pp 2981–2998

    Google Scholar 

  11. Gillespie R (1991) Manufacturing knowledge: a history of the Hawthorne experiments. Cambridge University Press, Cambridge

    Google Scholar 

  12. Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99:7821–7826

    Article  MATH  MathSciNet  Google Scholar 

  13. Granovetter MS (1973) The strength of weak ties. Am J Sociol 78(6):1360–1380

    Article  Google Scholar 

  14. Hansen MT (1999) The search-transfer problem: the role of weak ties in sharing knowledge across organization subunits. Adm Sci Q 44(1):82–111

    Article  Google Scholar 

  15. Hansen DL, Shneiderman B, Smith MA (2011) Analyzing social media networks with NodeXL: insights from a connected world. Elsevier, Burlington

    Google Scholar 

  16. http://gephi.github.io/

  17. http://www.cytoscape.org/

  18. http://www.lanl.gov/about/facts-figures/talent.php

  19. Karagiannis T, Vojnovic M (2008) Email information flow in large-scale enterprises. http://research.microsoft.com/pubs/70586/tr-2008-76.pdf

  20. Kilduff M, Tsai W (2003) Social networks and organizations. Sage, London

    Google Scholar 

  21. Kossinets G, Watts DJ (2006) Empirical analysis of an evolving social network. Science 311:88–90

    Google Scholar 

  22. Krackhardt D (1992) The strength of strong ties: the importance of Philos in organizations. In: Nohria N, Eccles RG (eds) Networks and organizations: structure, form, and action. Harvard Business School Press, Boston

    Google Scholar 

  23. Krackhardt D, Hanson JR (1993) Informal networks: the company behind the chart. Harvard Bus Rev 71(4):104–111

    Google Scholar 

  24. Leskovec J, Kleinberg J, Faloutsos C (2007) Graph evolution: densification and shrinking diameters. ACM Trans Knowl Discov Data 1(2)

    Google Scholar 

  25. Markus ML (1994) Electronic mail as the medium of managerial choice. Organ Sci 5:502–527

    Article  Google Scholar 

  26. Mitzenmacher M (2004) A brief history of generative models for power-law and lognormal distributions. Internet Math 1:226–251

    Article  MATH  MathSciNet  Google Scholar 

  27. Papadopoulos F, Kitsak M, Serrano MA, Boguna M, Krioukov D (2012) Popularity versus similarity in growing networks. Nature 489:537–540

    Article  Google Scholar 

  28. Phelps C, Heidl R, Wadwha A (2012) Knowledge, networks, and knowledge networks: a review and research agenda. J Manag 38:1115–1166

    Google Scholar 

  29. Polanyi M (1966) The tacit dimension. Doubleday, Garden City

    Google Scholar 

  30. Ravasz E, Barabasi A-L (2003) Hierarchical organization in complex networks. Phys Rev E 67:026112

    Article  Google Scholar 

  31. Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabasi A-L (2002) Hierarchical organization of modularity in metabolic networks. Science 297:1551–1555

    Article  Google Scholar 

  32. Sims BH, Sinitsyn N, Eidenbenz SJ (2013) Visualization and modeling of structural features of a large organizational email network. In: Proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and mining. ACM, New York, pp 787–791

    Google Scholar 

  33. Tortoriello M, Reagans R, McEvily B (2012) Bridging the knowledge gap: the influence of strong ties, network cohesion, and network range on the transfer of knowledge between organizational units. Organ Sci 4:1024–1039

    Article  Google Scholar 

  34. Wuchty S, Uzzi B (2011) Human communication dynamics in digital footsteps: a study of the agreement between self-reported ties and email networks. PLoS ONE 6(11):e26972

    Article  Google Scholar 

  35. Zeini S, Göhnert T, Hoppe U, Krempel L (2012) The impact of measurement time on subgroup detection in online communities. In: Proceedings of the 2012 IEEE/ACM international conference on advances in social networks analysis and mining. IEEE, Los Alamitos, pp 389–394

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Los Alamos National Laboratory, Los Alamos, NM, 87545, USA

    Benjamin H. Sims, Nikolai Sinitsyn & Stephan J. Eidenbenz

Authors
  1. Benjamin H. Sims
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nikolai Sinitsyn
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stephan J. Eidenbenz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Benjamin H. Sims .

Editor information

Editors and Affiliations

  1. Département d'Informatique et Ingénirie, Université du Québec en Outaouais, Gatineau, Québec, Canada

    Rokia Missaoui

  2. Département de Mathématiques et Informatique, Université Cheikh Anta Diop, Dakar, Senegal

    Idrissa Sarr

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland (outside the USA)

About this chapter

Cite this chapter

Sims, B.H., Sinitsyn, N., Eidenbenz, S.J. (2014). Hierarchical and Matrix Structures in a Large Organizational Email Network: Visualization and Modeling Approaches. In: Missaoui, R., Sarr, I. (eds) Social Network Analysis - Community Detection and Evolution. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-12188-8_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-12188-8_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12187-1

  • Online ISBN: 978-3-319-12188-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation