The structure of global centrality measures

Lozares, Carlos; López-Roldán, Pedro; Bolíbar, Mireia

doi:10.1080/13645579.2014.888238

Cited by 19 publications

(15 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A frequently investigated question in this context deals with correlations among centrality indices (Bolland, 1988;Rothenberg, Potterat, Woodhouse, Darrow, Muth, and Klovdahl, 1995;Lee, 2006;Valente, Coronges, Lakon, and Costenbader, 2008;Batool and Niazi, 2014;Li, Li, Van Mieghem, Stanley, and Wang, 2015;Lozares, López-Roldán, Bolibar, and Muntanyola, 2015). The underlying assumption being that correlations are a consequence of the formal definition of indices and thus highlight differences in the conceptualization of centrality.…”

Section: Introductionmentioning

confidence: 99%

Correlations among centrality indices and a class of uniquely ranked graphs

2017

View full text Add to dashboard Cite

Various centrality indices have been proposed to capture different aspects of structural importance but relations among them are largely unexplained. The most common strategy appears to be the pairwise comparison of centrality indices via correlation. While correlation between centralities is often read as an inherent property of the indices, we argue that it is confounded by network structure in a systematic way. In fact, correlations may be even more indicative of network structure than relations among indices. This has substantial implications for the interpretation of centrality effects as it implies that competing explanations embodied in different indices cannot be separated from each other if the observed network has a star-like property.

show abstract

Section: Introductionmentioning

confidence: 99%

Correlations among centrality indices and a class of uniquely ranked graphs

2017

View full text Add to dashboard Cite

show abstract

“…In this context LTR and other measures as the proposed before should be compared with independent cascade based rankings to see which one provides the best estimator for the spread of influence. In such context we plan to use the above measures together with the proposed in [27,28,29] to perform a study on the impact of the first k ranked nodes as measure of centrality. Those results should also be compared to the LTC measure proposed in this paper.…”

Section: Discussionmentioning

confidence: 99%

“…So far we have mentioned centrality measures to rank the central users of the network. However, although less well known, there are also centralization measures, also known as hierarchical measures [29]. These measures aim to determine to what extent the entire network has a centralized structure.…”

Section: Introductionmentioning

confidence: 99%

Centrality measure in social networks based on linear threshold model

Riquelme

González-Cantergiani²,

Molinero

et al. 2018

Knowledge-Based Systems

View full text Add to dashboard Cite

Centrality and influence spread are two of the most studied concepts in social network analysis. In recent years, centrality measures have attracted the attention of many researchers, generating a large and varied number of new studies about social network analysis and its applications. However, as far as we know, traditional models of influence spread have not yet been exhaustively used to define centrality measures according to the influence criteria. Most of the considered work in this topic is based on the independent cascade model. In this paper we explore the possibilities of the linear threshold model for the definition of centrality measures to be used on weighted and labeled social networks. We propose a new centrality measure to rank the users of the network, the Linear Threshold Rank (LTR), and a centralization measure to determine to what extent the entire network has a centralized structure, the Linear Threshold Centralization (LTC). We appraise the viability of the approach through several case studies. We consider four different social networks to compare our new measures with two centrality measures based on relevance criteria and another centrality measure based on the independent cascade model. Our results show that our measures are useful for ranking actors and networks in a distinguishable way.Peer ReviewedPostprint (author's final draft

show abstract

“…The structure of the networks was assessed by size, or number of actors in the network, density (actual number of ties / possible number of ties), and three statistics proposed by Burt (1992) to assess the effects of structural holes, with the rational that a network with more structural holes provides more opportunities: (1) constraint, referring to how much room one has to negotiate or exploit potential structural holes, (2) effective size, or number of alters, minus the redundancy in the network and (3) efficiency, or effective size / actual size. In addition, we computed surrogate global centrality estimators as described by Lozares, López-Roldán, Bolibar, & Muntanyola, (2015), summarizing closeness and betweenness centrality of all alters in an ego-network using the mean and max statistics and the centralization function (Butts, 2014; Freeman, 1979). We also computed ego betweenness centrality (in the ego-network) which is correlated to ego betweenness in the whole network (Everett & Borgatti, 2005).…”

Section: Methodsmentioning

confidence: 99%

Relationship of Social Network to Protective Factors in Suicide and Alcohol Use Disorder Intervention for Rural Yup’ik Alaska Native Youth

Philip

Ford

Henry

et al. 2016

Psychosocial Intervention

View full text Add to dashboard Cite

Suicide and alcohol use disorders are significant Alaska Native health disparities, yet there is limited understanding of protection and no studies of social network factors in protection in this or other populations. The Qungasvik intervention enhances protective factors from suicide and alcohol use disorders through activities grounded in Yup’ik cultural practices and values. Identification of social network factors associated with protection within the cultural context of these tight, close knit, and high density rural Yup’ik Alaska Native communities in southwest Alaska can help identify effective prevention strategies for suicide and alcohol use disorder risk. Using data from ego-centered social network and protective factors from suicide and alcohol use disorders surveys with 50 Yup’ik adolescents, we provide descriptive data on structural and network composition variables, identify key network variables that explain major proportions of the variance in a four principal component structure of these network variables, and demonstrate the utility of these key network variables as predictors of family and community protective factors from suicide and alcohol use disorder risk. Connections to adults and connections to elders, but not peer connections, emerged as predictors of family and community level protection, suggesting these network factors as important intervention targets for intervention.

show abstract

The structure of global centrality measures

Cited by 19 publications

References 26 publications

Correlations among centrality indices and a class of uniquely ranked graphs

Correlations among centrality indices and a class of uniquely ranked graphs

Centrality measure in social networks based on linear threshold model

Relationship of Social Network to Protective Factors in Suicide and Alcohol Use Disorder Intervention for Rural Yup’ik Alaska Native Youth

Contact Info

Product

Resources

About