Katz centrality

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

Demonstration of dynamic targeting in an online social medium

Abstract: A novel way of calculating online influence has been proposed in [2,1]. Bloom Agency have created new online software capable of collecting social data and calculating these new influence metrics in real time. A demonstration of this software will be given at the conference. Delegates will be encouraged to Tweet using the #socinfo2012 hashtag and the influence of the top ten Tweeters will be shown, along with a visualisation of the evolving conversation.

Centrality study and empirical analysis of microblog network

Abstract: This paper introduces the calculation method of degree centrality, closeness centrality and betwenness centrality based on the Sina weibo user data of Fudan University in Dec, 2012. The cumulative probability distribution of each centrality indicator is measured and analyzed. The relationship among three centrality metrics is also discussed. It is concluded that the combination of the three centrality measurements may effectively discover important nodes in the microblog network.

A Study of Centrality Measures in SNA

Abstract: In recent years, social network theory becomes more and more significant in social science. Basing on the fast-growing social network theory, SNA (Social Network Analysis) is also widely used and published in different journals. As social actors are like nodes in the network, we use centrality to measure these nodes in power, activity and communication convenience etc.. Degree centrality, betweenness centrality and closeness centrality are main detailed measurement, and they have different algorithm. In SNA study, the research purpose determines the selection of centrality; and the use of these three centralities constitutes an important part in SNA study.

Curve Estimation among Citation and Centrality Measures in Article-level Citation Networks

Abstract: The characteristics of citation and centrality measures in citation networks can be identified using multiple linear regression analyses. In this study, we examine the relationships between bibliometric indices and centrality measures in an article-level co-citation network to determine whether the linear model is the best fitting model and to suggest the necessity of data transformation in the analysis. 703 highly cited articles in Physics published in 2004 were sampled, and four indicators were developed as variables in this study: citation counts, degree centrality, closeness centrality, and betweenness centrality in the co-citation network. As a result, the relationship pattern between citation counts and degree centrality in a co-citation network fits a non-linear rather than linear model. Also, the relationship between degree and closeness centrality measures, or that between degree and betweenness centrality measures, can be better explained by non-linear models than by a linear model. It may be controversial, however, to choose non-linear models as the best-fitting for the relationship between closeness and betweenness centrality measures, as this result implies that data transformation may be a necessary step for inferential statistics.

Selection of most relevant centrality measures: A systematic survey on protein-protein interaction networks

Abstract: Background: Several centrality measures were introduced to identify "central" nodes in large networks, which are the most important vertices within a graph. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of the network. On the other hand, the user can easily ignore the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures. Results: We used yeast PPINs to compare 27 common of centrality measures. The measures characterize and assort influential nodes of the networks. We applied principal component analysis (PCA) and hierarchical clustering algorithm and found that the most informative measures depend on the networks topology. Interestingly, some measures had a high level of contribution in comparison to others in all PPINs, namely Latora closeness, Decay, Lin, Freeman closeness, Diffusion, Residual closeness and Average distance centralities. Finally, using clustering analysis, we restated that the determination of important nodes depends on the network topology. Conclusions: The choice of a suitable set of centrality measures is crucial for inferring important functional properties of a network. We conferred about why it should be treated with various centrality measures as well as variables of a network. We concluded that unsupervised machine learning methods conduce to undertake a data reduction and thus choosing appropriate variables (centrality measures). Hence, we proposed identifying the contribution proportions of the centrality measures with PCA as a prerequisite step of network analysis before inferring functional consequences, e.g., essentiality of a node.