Katz centrality

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

Modeling and measuring graph similarity: the case for centrality distance

Abstract: The study of the topological structure of complex networks has fascinated researchers for several decades, and today we have a fairly good understanding of the types and reoccurring characteristics of many different complex networks. However, surprisingly little is known today about models to compare complex graphs, and quantitatively measure their similarity. This paper proposes a natural similarity measure for complex networks: centrality distance, the difference between two graphs with respect to a given node centrality. Centrality distances allow to take into account the specific roles of the different nodes in the network, and have many interesting applications. As a case study, we consider the closeness centrality in more detail, and show that closeness centrality distance can be used to effectively distinguish between randomly generated and actual evolutionary paths of two dynamic social networks.

Identifying key papers within a journal via network centrality measures

Abstract: This article examines the extent to which existing network centrality measures can be used (1) as filters to identify a set of papers to start reading within a journal and (2) as article-level metrics to identify the relative importance of a paper within a journal. We represent a dataset of published papers in the Public Library of Science (PLOS) via a co-citation network and compute three established centrality metrics for each paper in the network: closeness, betweenness, and eigenvector. Our results show that the network of papers in a journal is scale-free and that eigenvector centrality (1) is an effective filter and article-level metric and (2) correlates well with citation counts within a given journal. However, closeness centrality is a poor filter because articles fit within a small range of citations. We also show that betweenness centrality is a poor filter for journals with a narrow focus and a good filter for multidisciplinary journals where communities of papers can be identified.

A return spillover network perspective analysis of Chinese financial institutions’ systemic importance

Abstract: The objective of this study is to analyze systemic importance of Chinese financial institutions and its influential factors based on return spillover network. We first investigate the return spillover effects among financial institutions and construct the return spillover networks by Granger causality in vector autoregressive (VAR) models. Then we calculate six network centralities (degree centrality, closeness centrality, betweenness centrality, modified Katz centrality, eccentricity centrality and information centrality) to measure systemic importance of financial institutions. Because different centrality measures are correlated with each other, we use the principal component analysis method to obtain comprehensive information about systemic importance of financial institutions. Finally, we identify the major factors, including market and accounting variables, which affect systemic importance of the financial institutions with panel data regression analysis. We find that financial institutions with larger tail risk of stock return, higher return on equity, lower turnover rate and lower assets growth rate tend to be associated with greater systemic importance.

Effective co-betweenness centrality computation

Abstract: Betweenness centrality of vertices is essential in the analysis of social and information networks, and co-betweenness centrality is one of two natural ways to extend it to sets of vertices. Existing algorithms for co-betweenness centrality computation suffer from at least one of the following problems: i) their applicability is limited to special cases like sequences, sets of size two, and ii) they are not efficient in terms of time complexity. In this paper, we present efficient algorithms for co-betweenness centrality computation of any set or sequence of vertices in weighted and unweighted networks. We also develop effective methods for co-betweenness centrality computation of sets and sequences of edges. These results provide a clear and extensive view about the complexity of co-betweenness centrality computation for vertices and edges in weighted and un-weighted networks. Finally, we perform extensive experiments on real-world networks from different domains including social, information and communication networks, to show the empirical efficiency of the proposed methods.

Identification of top-K influential communities in big networks

Abstract: Because communities are the fundamental component of big data/large data network graphs, community detection in large-scale graphs is an important area to study. Communities are a collection of a set of nodes with similar features. In a given graph there can be many features for clustering the nodes to form communities. Varying the features of interest can form several communities. Out of all communities that are formed, only a few communities are dominant and most influential for a given network graph. This might well contain influential nodes; i.e., for each possible feature of clustering, there will be only a few influential communities in the graph. Identification of such communities is a salient subject for research. This paper present a technique to identify the top-K communities, based on the average Katz centrality of all the communities in a network of communities and the distinctive nature of the communities. One can use these top-K communities to spread information efficiently into the network, as these communities are capable of influencing neighboring communities and thus spreading the information into the network efficiently.