A Graph-theoretic perspective on centrality

Borgatti, Stephen P.; Everett, Martin G.

doi:10.1016/j.socnet.2005.11.005

Cited by 1,304 publications

(873 citation statements)

References 46 publications

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“…The reader can find more details about centrality measures in the excellent review of Borgatti and Everett (2006) as well as in the Chapter 5 of the book by Wasserman and Faust (1994).…”

Section: On Walk-based Network Measuresmentioning

confidence: 99%

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Generalized walks-based centrality measures for complex biological networks

Estrada

2010

Journal of Theoretical Biology

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A strategy for zooming in and out the topological environment of a node in a complex network is developed. This approach is applied here to generalize the subgraph centrality of nodes in complex networks. In this case the zooming in strategy is based on the use of some known matrix functions which allow focusing locally on the environment of a node. When a zooming out strategy is applied new matrix functions are introduced, which give a more global picture of the topological surrounds of a node. These indices permit a modulation of the scales at which the environment of a node influences its centrality. We apply them to the study of 10 protein-protein interaction (PPI) networks. We illustrate the similarities and differences between the generalized subgraph centrality indices as well as among them and some classical centrality measures. We show here that the use of centrality indices based on the zooming in strategy identifies a larger number of essential proteins in the yeast PPI network than any of the other centrality measures studied.

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“…The reader can find more details about centrality measures in the excellent review of Borgatti and Everett (2006) as well as in the Chapter 5 of the book by Wasserman and Faust (1994).…”

Section: On Walk-based Network Measuresmentioning

confidence: 99%

“…It is worth mentioning that several other successful centrality measures are not based on walks, such as the closeness, betweenness, and information centrality (Borgatti and Everett, 2006). …”

Section: On Walk-based Network Measuresmentioning

confidence: 99%

Generalized walks-based centrality measures for complex biological networks

Estrada

2010

Journal of Theoretical Biology

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“…where C is the highest degree of centrality of all cities in the agglomeration and C is the degree centrality of city i [40].…”

Section: Degree Centrality and Centralization Measurementmentioning

confidence: 99%

Spatial Pattern and Evolution of Urban System Based on Gravity Model and Whole Network Analysis in the Huaihe River Basin of China

Fan

Zhang²,

et al. 2018

Discrete Dynamics in Nature and Society

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The spatial pattern and evolution of urban system have been hot research issues in the field of urban research. In this paper, the network analysis method based on the gravity model and the related measurements were used to reveal the properties of the spatial pattern and evolution of the urban system in the HRB (Huaihe River Basin) of China. The findings of this study are as follows: During the period from 2006 to 2014, the economic contact between the HRB cities has been strengthened, but the differences between cities have been expanding. In general, the HRB cities have not yet formed a close network structure, and a trend of economic integration has not been found. This paper expresses the spatial pattern and evolution of urban system in an intuitive way and helps to explain the evolution mechanism of urban system. The method was confirmed by empirical research. Because of the operational and visual expression, this method has broad application prospects in the urban system research.

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“…There are different approaches to measure centrality used in the social network area, mostly inherited from graph centrality (Nieminen, 1974;Freeman, 1979;Freeman et al, 1991;Borgatti and Everett, 2006;Latora and Marchiori, 2007). Freeman (1979) describes in a general review three types of centrality as developed in social network research: degree, betweenness and closeness centrality.…”

Section: Individual User Models Cognitive Centralitymentioning

confidence: 99%

Adaptive notifications to support knowledge sharing in close-knit virtual communities

Kleanthous

Dimitrova

2012

User Model User-Adap Inter

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Social web-groups where people with common interests and goals communicate, share resources, and construct knowledge, are becoming a major part of today's organisational practice. Research has shown that appropriate support for effective knowledge sharing tailored to the needs of the community is paramount. This brings a new challenge to user modelling and adaptation, which requires new techniques for gaining sufficient understanding of a virtual community and identifying areas where the community may need support. The research presented here addresses this challenge presenting a novel computational approach for community-tailored support underpinned by organisational psychology and aimed at facilitating the functioning of the community as a whole (i.e. as an entity). A framework describing how key community processes -transactive memory, shared mental models, and cognitive centrality -can be utilised to derive knowledge sharing patterns from community log data is described. The framework includes two parts: (i) extraction of a community model that represents the community based on the key processes identified and (ii) identification of knowledge sharing behaviour patterns that are used to generate adaptive notifications. Although the notifications target individual members, they aim to influence individuals' behaviour in a way that can benefit the functioning of the community as a whole. A validation study has been performed to examine the effect of community-adapted notifications on individual members and on the community as a whole using a close-knit community of researchers sharing references. The study shows that notification messages can improve members' awareness and perception of how they relate to other members in the community. Interesting observations have been made about the linking between the physical and the virtual community, and how this may influence members' awareness and knowledge sharing behaviour. Broader implications for using log data to derive community models based on key community processes and generating community-adapted notifications are discussed.

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A Graph-theoretic perspective on centrality

Cited by 1,304 publications

References 46 publications

Generalized walks-based centrality measures for complex biological networks

Generalized walks-based centrality measures for complex biological networks

Spatial Pattern and Evolution of Urban System Based on Gravity Model and Whole Network Analysis in the Huaihe River Basin of China

Adaptive notifications to support knowledge sharing in close-knit virtual communities

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