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Katz centrality

About: Katz centrality is a research topic. Over the lifetime, 601 publications have been published within this topic receiving 77858 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the correlation between centrality metrics in terms of their Pearson correlation coefficient and their similarity in ranking of nodes was studied. And the effect of inflexible contrarians selected based on different centrality measures in helping one opinion to compete with another in the inflexibility contrarian opinion (ICO) model was investigated.
Abstract: In recent decades, a number of centrality metrics describing network properties of nodes have been proposed to rank the importance of nodes. In order to understand the correlations between centrality metrics and to approximate a high-complexity centrality metric by a strongly correlated low-complexity metric, we first study the correlation between centrality metrics in terms of their Pearson correlation coefficient and their similarity in ranking of nodes. In addition to considering the widely used centrality metrics, we introduce a new centrality measure, the degree mass. The mth-order degree mass of a node is the sum of the weighted degree of the node and its neighbors no further than m hops away. We find that the betweenness, the closeness, and the components of the principal eigenvector of the adjacency matrix are strongly correlated with the degree, the 1st-order degree mass and the 2nd-order degree mass, respectively, in both network models and real-world networks. We then theoretically prove that the Pearson correlation coefficient between the principal eigenvector and the 2nd-order degree mass is larger than that between the principal eigenvector and a lower order degree mass. Finally, we investigate the effect of the inflexible contrarians selected based on different centrality metrics in helping one opinion to compete with another in the inflexible contrarian opinion (ICO) model. Interestingly, we find that selecting the inflexible contrarians based on the leverage, the betweenness, or the degree is more effective in opinion-competition than using other centrality metrics in all types of networks. This observation is supported by our previous observations, i.e., that there is a strong linear correlation between the degree and the betweenness, as well as a high centrality similarity between the leverage and the degree.

90 citations

Journal ArticleDOI
TL;DR: A novel form of centrality is introduced: the second order centrality which can be computed in a distributed manner which provides locally each node with a value reflecting its relative criticity and relies on a random walk visiting the network in an unbiased fashion.

89 citations

ReportDOI
TL;DR: In this article, a simple model of diffusion shows how boundedly rational individuals can, just by tracking gossip about people, identify those who are most central in a network according to diffusion centrality (a measure of network centrality which nests existing ones, and predicts the extent to which piece of information seeded to a network member diffuses in finite time).
Abstract: Is it possible, simply by asking a few members of a community, to identify individuals who are best placed to diffuse information? A simple model of diffusion shows how boundedly rational individuals can, just by tracking gossip about people, identify those who are most central in a network according to "diffusion centrality" (a measure of network centrality which nests existing ones, and predicts the extent to which piece of information seeded to a network member diffuses in finite time). Using rich network data from 35 Indian villages, we find that respondents accurately nominate those who are diffusion central -- not just traditional leaders or those with many friends. In a subsequent randomized field experiment in 213 villages, we track the diffusion of a piece of information initially given to a small number of "seeds" in each community. Seeds who are nominated by others lead to a near tripling of the spread of information relative to randomly chosen seeds. Diffusion centrality accounts for some, but not all, of the extra diffusion from these nominated seeds compared to other seeds (including those with high social status) in our experiment.

84 citations

Proceedings ArticleDOI
28 Jun 2009
TL;DR: Both feature analysis and experimental comparative studies revealed the general profile of selected measures of centrality in the social network profile.
Abstract: Network analysis offers many centrality measures that are successfully utilized in the process of investigating the social network profile. The most important and representative measures are presented in the paper. It includes indegree centrality, proximity prestige, rank prestige, node position, outdegree centrality, eccentrality, closeness centrality, and betweenes centrality. Both feature analysis and experimental comparative studies revealed the general profile of selected measures.

83 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202318
202232
202114
202013
201919
201824