Centrality in organizational networks

Grassi, Rosanna; Stefani, Silvana; Torriero, Anna

doi:10.1002/int.20400

Cited by 15 publications

(15 citation statements)

References 15 publications

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“…We have also shown how to generate these distributions when the whole graph is synthetic. Future work includes the development of other algorithms that include some additional constraints into the synthetic graph, for example, adding constraints on centrality, girth, diameter, etc.…”

Section: Discussionmentioning

confidence: 99%

Synthetic generation of spatial graphs

Torra

Jonsson

Navarro‐Arribas

et al. 2018

Int. J. Intell. Syst.

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Graphs can be used to model many different types of interaction networks, for example, online social networks or animal transport networks. Several algorithms have thus been introduced to build graphs according to some predefined conditions. In this paper, we present an algorithm that generates spatial graphs with a given degree sequence. In spatial graphs, nodes are located in a space equiped with a metric. Our goal is to define a graph in such a way that the nodes and edges are positioned according to an underlying metric. More particularly, we have constructed a greedy algorithm that generates nodes proportional to an underlying probability distribution from the spatial structure, and then generates edges inversely proportional to the Euclidean distance between nodes. The algorithm first generates a graph that can be a multigraph, and then corrects multiedges. Our motivation is in data privacy for social networks, where a key problem is the ability to build synthetic graphs. These graphs need to satisfy a set of required properties (e.g., the degrees of the nodes) but also be realistic, and thus, nodes (individuals) should be located according to a spatial structure and connections should be added taking into account nearness.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Discussionmentioning

confidence: 99%

Synthetic generation of spatial graphs

Torra

Jonsson

Navarro‐Arribas

et al. 2018

Int. J. Intell. Syst.

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“…, where g uw is the number of geodesics from u to w, and g uw (v) is the number of geodesics between u and w passing through v, (see Grassi et al 2010a for an application of betweenness to an organizational network and Grassi et al 2009 for some theoretical results). It is convenient to normalize the betweenness dividing b(v) by its maximum possible value n−1 2 , so that the normalized betweenness lies in the…”

Section: Univariate Networkmentioning

confidence: 99%

“…The first phase is described in detail in Grassi et al (2010a). This paper describes the instruments, methodology and results related to the second phase.…”

Section: The Case Studymentioning

confidence: 99%

Informal ties in organizations: a case study

D’Errico

Stefani

Torriero³

2013

Qual Quant

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“…To be more specific, Social Network Analysis (SNA) is a technique of quantitative research for social networks, based on the pillars of graph theory, which allows the attainment of a technical-methodological approximation to explore the relationships and interdependent actions among the different members of a social system. SNA can be used to investigate the structure of organizations, as well as to describe formal and informal relationships among individuals in the organization (Grassi et al., 2010). As a technique, SNA employs the use of actor-network representations to understand social phenomena.…”

Section: Introductionmentioning

confidence: 99%

Analysis of organizational power networks through a holistic approach using consensus strategies

Rámos

Franco-Crespo

González-Pérez

et al. 2019

Heliyon

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Power is one of the most complex organizational attributes to understand due to the multiple related variables and dimensions in which it appears. The ownership and use and of power are reflected in the interpersonal relationships within an organization, as a result, modeling its structure and interactions can lead to knowledge about the power networks that shape it. The objective of this study was to identify the behavior of organizational networks based on existing sources of power, using a consensual analysis of the different topologies present in these networks. The study was carried out in a private production company in Ecuador, which has representation at a domestic level. To this end, a 12-question personalized questionnaire was designed with the aim of identifying specific networks and was applied to 1190 workers in the company. The results were obtained using organizational network analysis and a consensus strategy to integrate the centralities found in multiple networks into one. This study can serve as a reference to organizations, so they can know the relationships between people within it, as part of their management process. In this way, the identification of people within power networks is useful for knowing the “key” actors in the promotion of organizational changes, as well as for the development of career plans based on the position that people occupy in the organizational system.

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Centrality in organizational networks

Abstract: In this paper we discuss the role of centrality in organizational networks. We will present some new results related to the different concepts of centrality. A case study of an ICT consulting company concludes.

Cited by 15 publications

References 15 publications

Synthetic generation of spatial graphs

Synthetic generation of spatial graphs

Informal ties in organizations: a case study

Analysis of organizational power networks through a holistic approach using consensus strategies

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