Importance Of Network Analysis

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Introduction
Network analysis has been adopted across the scientific spectrum from the social sciences to biochemisty with applications in empirical research, modelling, and management, to name a few.1,2,3,4 While the network structure of operating sub-groups has been examined previously to our knowledge a comprehensive analysis of the operating suite incorporating all relevant participants has not yet occurred.5 In studying a network several definitions are worth reviewing (Table 1). Networks can be directed or undirected, referring to whether an edge has a defined source and target or merely denotes the existence of a connection, and weighted or unweighted, referring to value attributed to an edge to impart information related to the nature of a tie. From the structure that arises out of nodes and their corresponding edges several traits addressing their importance in a network can be discussed.9 Though many and varied measures exist, perhaps the most commonly discussed centralities are degree, closeness, betweenness, and eigenvector and it is to these we will limit our analysis. Further, the empirical detection of existing sub-groups or “communities” in a the network will also be examined. While clustering coefficient, a common metric of the social embeddedness of a node in a network, can be applied to undirected networks it cannot be applied to weighted networks and thus was not examined.10

Centrality Measures and Weighting
Expanding upon Table 1, it is worth mentioning some aspects of the various centralities and how they relate. Nodes with high degree centrality are connected directly to correspondingly greater portions of the network and are able to transfer information quickly.7 Nodes with high betweenness centrality can...

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...robability that two distinct communities can be merged by moving nodes one by one is very low.”22 We include this caution so that others wishing to apply similar techniques to their own organizational structures are explicitly aware of potential limitations of community detection.

Future Research
The use of these centrality measures may inform and guide how new informational changes may be best implemented. Analysis across a variety of instution types and sizes would be useful in identifying any generalizable management concepts. Also, subgroup and subcommunity analysis may allow for the identification of key structures that would affect tacit and explicit information flow. For example, this might be useful at an academic center where the identification and optimization of tacit information flow could improve and make more consistent the education of its residents.

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