When testing centrality in random graphs it is of importance to specify models that capture the irregularities in the structure due to centrality. In this paper we propose using the well-known block models in an attempt to capture such irregularities. The baseline model, revealing no centrality structure, used in this paper is the Bernoulli model. It is shown that the maximum likelihood estimat...

Functional magnetic resonance data acquired in a task-absent condition ("resting state") require new data analysis techniques that do not depend on an activation model. In this work, we introduce an alternative assumption- and parameter-free method based on a particular form of node centrality called eigenvector centrality. Eigenvector centrality attributes a value to each voxel in the brain su...

In addition to science citation indicators of journals like impact and immediacy, social network analysis provides a set of centrality measures like degree, betweenness, and closeness centrality. These measures are first analyzed for the entire set of 7,379 journals included in the Journal Citation Reports of the Science Citation Index and the Social Sciences Citation Index 2004, and then also ...

Node centrality measures including degree, eigenvector, Katz and subgraph centralities are analyzed for both undirected and directed networks. We show how parameter-dependent measures, such as Katz and subgraph centrality, can be “tuned” to interpolate between degree and eigenvector centrality, which appear as limiting cases of the other measures. We interpret our finding in terms of the local ...

BACKGROUND AND OBJECTIVES Following stressful events, there is evidence that the degree to which the event is perceived as central to one's identity and forms a reference point for the attribution of meaning to other events (i.e., "centrality of events") is associated with depression symptoms. However, these findings have primarily come from cross-sectional studies of undergraduate samples, and...

Existing centrality measures for social network analysis suggest the importance of an actor and give consideration to actor’s given structural position in a network. These existing measures suggest specific attribute of an actor (i.e., popularity, accessibility, and brokerage behavior). In this study, we propose new hybrid centrality measures (i.e., Degree-Degree, Degree-Closeness and DegreeBet...

In this paper, we present and compare various centrality measures for graphbased keyphrase extraction. Through experiments carried out on three standard datasets of different languages and domains, we show that simple degree centrality achieve results comparable to the widely used TextRank algorithm, and that closeness centrality obtains the best results on short documents.

19 We investigated the reliability of nodal network metrics of functional connectivity (FC) 20 networks of MEG covering the whole brain at the sensor level in the eyes-closed (EC) and 21 eyes-open (EO) resting states. Mutual information (MI) was employed as a measure of FC 22 between sensors in theta, alpha, beta, and gamma frequency bands of MEG signals. MI matrices 23 were assessed with three...

The high-level contribution of this paper is a comprehensive analysis of the correlation levels between node centrality (a computationally light-weight metric) and maximal clique size (a computationally hard metric) in random network and scale-free network graphs generated respectively from the well-known Erdos-Renyi (ER) and Barabasi-Albert (BA) models. We use three well-known measures for eva...

BACKGROUND The analysis of co-authorship network aims at exploring the impact of network structure on the outcome of scientific collaborations and research publications. However, little is known about what network properties are associated with authors who have increased number of joint publications and are being cited highly. METHODOLOGY/PRINCIPAL FINDINGS Measures of social network analysis...