Measuring agility of networked organizational structures via network entropy and mutual information

Yuan Lin, Kevin C. Desouza, Sumit Roy

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

While the agility of networked organizational structures is important for organizational performance, studies on how to evaluate it remain scant, probably because the difficulty in measuring network evolution. In this conceptual paper, we propose two measures - network entropy and mutual information - to characterize the agility of networked organizational structure. Rooted in graph theory and information theory, these two measures capture network evolution in a comprehensive and parsimonious way. They indicate the uncertainty (or disorder) at the network level as well as the degree distribution at the individual level. We also propose an algorithm for applying them in the scenario of adding links to a network while holding the number of nodes fixed. Both simulated and real networks are used for demonstration. Implications and areas for future research are discussed in the end.

Original languageEnglish (US)
Pages (from-to)2824-2836
Number of pages13
JournalApplied Mathematics and Computation
Volume216
Issue number10
DOIs
StatePublished - Jul 15 2010
Externally publishedYes

Fingerprint

Graph theory
Information theory
Mutual Information
Network Structure
Entropy
Demonstrations
Network Evolution
Degree Distribution
Information Theory
Disorder
Uncertainty
Scenarios
Evaluate
Vertex of a graph

Keywords

  • Network entropy
  • Network evolution
  • Network measurement
  • Organization agility
  • Organizational networks

ASJC Scopus subject areas

  • Applied Mathematics
  • Computational Mathematics

Cite this

Measuring agility of networked organizational structures via network entropy and mutual information. / Lin, Yuan; Desouza, Kevin C.; Roy, Sumit.

In: Applied Mathematics and Computation, Vol. 216, No. 10, 15.07.2010, p. 2824-2836.

Research output: Contribution to journalArticle

Lin, Yuan ; Desouza, Kevin C. ; Roy, Sumit. / Measuring agility of networked organizational structures via network entropy and mutual information. In: Applied Mathematics and Computation. 2010 ; Vol. 216, No. 10. pp. 2824-2836.
@article{f58ad29c20544cda94fd39e94285db41,
title = "Measuring agility of networked organizational structures via network entropy and mutual information",
abstract = "While the agility of networked organizational structures is important for organizational performance, studies on how to evaluate it remain scant, probably because the difficulty in measuring network evolution. In this conceptual paper, we propose two measures - network entropy and mutual information - to characterize the agility of networked organizational structure. Rooted in graph theory and information theory, these two measures capture network evolution in a comprehensive and parsimonious way. They indicate the uncertainty (or disorder) at the network level as well as the degree distribution at the individual level. We also propose an algorithm for applying them in the scenario of adding links to a network while holding the number of nodes fixed. Both simulated and real networks are used for demonstration. Implications and areas for future research are discussed in the end.",
keywords = "Network entropy, Network evolution, Network measurement, Organization agility, Organizational networks",
author = "Yuan Lin and Desouza, {Kevin C.} and Sumit Roy",
year = "2010",
month = "7",
day = "15",
doi = "10.1016/j.amc.2010.03.132",
language = "English (US)",
volume = "216",
pages = "2824--2836",
journal = "Applied Mathematics and Computation",
issn = "0096-3003",
publisher = "Elsevier Inc.",
number = "10",

}

TY - JOUR

T1 - Measuring agility of networked organizational structures via network entropy and mutual information

AU - Lin, Yuan

AU - Desouza, Kevin C.

AU - Roy, Sumit

PY - 2010/7/15

Y1 - 2010/7/15

N2 - While the agility of networked organizational structures is important for organizational performance, studies on how to evaluate it remain scant, probably because the difficulty in measuring network evolution. In this conceptual paper, we propose two measures - network entropy and mutual information - to characterize the agility of networked organizational structure. Rooted in graph theory and information theory, these two measures capture network evolution in a comprehensive and parsimonious way. They indicate the uncertainty (or disorder) at the network level as well as the degree distribution at the individual level. We also propose an algorithm for applying them in the scenario of adding links to a network while holding the number of nodes fixed. Both simulated and real networks are used for demonstration. Implications and areas for future research are discussed in the end.

AB - While the agility of networked organizational structures is important for organizational performance, studies on how to evaluate it remain scant, probably because the difficulty in measuring network evolution. In this conceptual paper, we propose two measures - network entropy and mutual information - to characterize the agility of networked organizational structure. Rooted in graph theory and information theory, these two measures capture network evolution in a comprehensive and parsimonious way. They indicate the uncertainty (or disorder) at the network level as well as the degree distribution at the individual level. We also propose an algorithm for applying them in the scenario of adding links to a network while holding the number of nodes fixed. Both simulated and real networks are used for demonstration. Implications and areas for future research are discussed in the end.

KW - Network entropy

KW - Network evolution

KW - Network measurement

KW - Organization agility

KW - Organizational networks

UR - http://www.scopus.com/inward/record.url?scp=77953319894&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77953319894&partnerID=8YFLogxK

U2 - 10.1016/j.amc.2010.03.132

DO - 10.1016/j.amc.2010.03.132

M3 - Article

VL - 216

SP - 2824

EP - 2836

JO - Applied Mathematics and Computation

JF - Applied Mathematics and Computation

SN - 0096-3003

IS - 10

ER -