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Disciplinary Convergence in Systems Engineering Research pp 249–263Cite as

Understanding How Social Network Analysis Can Provide Insights Into Emergent Networks of Systems

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Abstract

The Department of Defense (DoD) is currently faced with a major challenge as individually designed and procured systems become more interoperable and dependent upon each other to provide value to the warfighter. The DoD currently has 79 Major Defense Acquisitions Programs (MDAP) in addition to the thousands of smaller programs and legacy systems that all operate on the battlefield together. DoD Systems engineers must understand how these systems interoperate on the battlefield to deliver the desired effects for the warfighter. However, traditional systems engineering and system of systems analysis tools and methods are inadequate to describe, visualize, and analyze the complex network of systems that have emerged over the past several decades. There is an opportunity to apply and adapt social network analysis (SNA) tools and methods to systems to understand the network, quantify the “ilities,” and anticipate the effect of changes to the network. This chapter examines systems engineering, systems architecture, and systems of systems (SoS) analysis to identify shortfalls in the method to deal with emergent networks of systems. It proposes a combination of individual system architectures to develop the network of systems. It goes on to understand how SNA tools and methods can be adapted to understand networks of systems and identify the potential for the quantification of some of the systems engineering “ilities” through SNA metrics. Finally, it discusses a proposed research path forward to further apply these tools to a more robust representation of the DoD network of systems, further analyze the potential application of SNA tools to networks of systems, and a method to validate and verify the results of this work.

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Abbreviations

C A :

Group centrality

C b(n i ):

Betweenness centrality of node i

C’c(n i ):

Closeness centrality of node i

C’d(n i ):

Degree centrality of node i

D :

Network density

d(n i ):

Connections for node i

d(n i, n j ):

Connections between node i and j

g :

Total nodes in network

g jk :

Edges between nodes j and k

n i :

Node i

N s :

Subgraph of network N

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Author information

Authors and Affiliations

  1. Stevens Institute of Technology, Hoboken, NJ, USA

    James R. Enos & Roshanak Nilchiani

Authors
  1. James R. Enos
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  2. Roshanak Nilchiani
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Corresponding author

Correspondence to James R. Enos .

Editor information

Editors and Affiliations

  1. Systems Architecting and Engineering Program, University of Southern California, Los Angeles, California, USA

    Azad M. Madni

  2. Center for Systems and Software Engineering, University of Southern California, Los Angeles, California, USA

    Barry Boehm

  3. Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, California, USA

    Roger G. Ghanem

  4. University of Southern California, Los Angeles, California, USA

    Daniel Erwin

  5. Systems Engineering Fellow, The Aerospace Corporation, El Segundo, California, USA

    Marilee J. Wheaton

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Enos, J.R., Nilchiani, R. (2018). Understanding How Social Network Analysis Can Provide Insights Into Emergent Networks of Systems. In: Madni, A., Boehm, B., Ghanem, R., Erwin, D., Wheaton, M. (eds) Disciplinary Convergence in Systems Engineering Research. Springer, Cham. https://doi.org/10.1007/978-3-319-62217-0_18

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  • DOI: https://doi.org/10.1007/978-3-319-62217-0_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62216-3

  • Online ISBN: 978-3-319-62217-0

  • eBook Packages: EngineeringEngineering (R0)

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