The Closeness Centrality Analysis of Fuzzy Social Network Based on Inversely Attenuation Factor

Hu, Renjie; Zhang, Guangyu; Liao, Liping

doi:10.1007/978-3-642-38667-1_46

Cited by 8 publications

(2 citation statements)

References 28 publications

(25 reference statements)

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“…Unfortunately, the parameter α is unknown, and must be estimated for a given sociomatrix. Hu et al [30] suggested an 'inversely attenuation factor' α = 1/k to adjust for the attenuation phenomenon of longer paths in a fuzzy social network. However, the inverse attenuation factor decreases too fast.…”

Section: Definition 31: Fuzzy Closeness Centrality Is the Sum Of Fuzmentioning

confidence: 99%

Closeness Centrality Measures in Fuzzy Enterprise Technology Innovation Cooperation Networks

Liao

Chen

et al. 2019

Fuzzy Information and Engineering

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Centrality analysis is one of the most important and commonly used tools in social networks. For social networks where edges are just present or absent and have no more information attached, many centrality measures have been presented, such as degree, closeness, betweenness, eigenvector and Laplacian centrality. There has been a growing need to design centrality measures for fuzzy enterprise technology innovation cooperation networks (FETICNs), because FETICNs where edges are attached with fuzzy technical cooperation relation would contain rich information. In this paper, we propose some new centrality measures called fuzzy logarithm attenuation closeness centrality and fuzzy logarithm attenuation closeness centralization which are applicable to the FETICNs. It unveils more structural information about fuzzy technical cooperation relation, attenuation factor, and connectivity of the FETICNs. Furthermore, we investigate the validness of a new centrality measure by illustrating this method to an experimental study and obtain reliable results, which provide strong evidences to the new measure's utility.

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Section: Definition 31: Fuzzy Closeness Centrality Is the Sum Of Fuzmentioning

confidence: 99%

Closeness Centrality Measures in Fuzzy Enterprise Technology Innovation Cooperation Networks

Liao

Chen

et al. 2019

Fuzzy Information and Engineering

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“…Liao et al [22] extended the notion of centrality to the fuzzy framework and proposed fuzzy degree centrality, fuzzy closeness centrality and fuzzy betweenness centrality, in undirected fuzzy social network. Hu et al [23] extended the notion of centrality and centralization to the fuzzy framework, proposed fuzzy inversely attenuation closeness centrality, and discussed fuzzy group closeness centralization based on inverse attenuation factor in undirected fuzzy social networks. Liao et al [24] extended structural holes theory to undirected fuzzy social networks, studied the method of calculating the number of structural holes that actors occupy and methods for their classification, and discussed their applications in fuzzy enterprises technological innovation network.…”

Section: Introductionmentioning

confidence: 99%

Structural Holes in Directed Fuzzy Social Networks

Zhang

2014

Journal of Applied Mathematics

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The structural holes have been a key issue in fuzzy social network analysis. For undirected fuzzy social networks where edges are just present or absent undirected fuzzy relation and have no more information attached, many structural holes measures have been presented, such as key fuzzy structural holes, general fuzzy structural holes, strong fuzzy structural holes, and weak fuzzy structural holes. There has been a growing need to design structural holes measures for directed fuzzy social networks, because directed fuzzy social networks where edges are attached by directed fuzzy relation would contain rich information. In this paper, we extend structural holes theory to directed fuzzy social network and propose the algorithm of unidirectional fuzzy structural holes and bidirectional fuzzy structural holes, which unveil more structural information of directed fuzzy social networks. Furthermore, we investigate the validness of the algorithm by illustrating this method to a case calledG-Y Research Teamand obtain reliable results, which provide strong evidences of the new measure’s utility.

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