Topic
Katz centrality
About: Katz centrality is a research topic. Over the lifetime, 601 publications have been published within this topic receiving 77858 citations.
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TL;DR: The presented incremental algorithm can achieve substantial performance speedup when compared to the state of the art and is a good predictor of betweenness centrality, which can be carried out on social network data sets with millions of nodes.
Abstract: The increasing availability of dynamically changing digital data that can be used for extracting social networks over time has led to an upsurge of interest in the analysis of dynamic social networks. One key aspect of dynamic social network analysis is finding the central nodes in a network. However, dynamic calculation of centrality values for rapidly changing networks can be computationally expensive, with the result that data are frequently aggregated over many time periods and only intermittently analyzed for centrality measures. This paper presents an incremental betweenness centrality algorithm that efficiently updates betweenness centralities or k-betweenness centralities of nodes in dynamic social networks by avoiding re-computations through the efficient storage of information from earlier computations. In this paper, we evaluate the performance of the proposed algorithms for incremental betweenness centrality and k-betweenness centrality on both synthetic social network data sets and on several real-world social network data sets. The presented incremental algorithm can achieve substantial performance speedup (3–4 orders of magnitude faster for some data sets) when compared to the state of the art. And, incremental k-betweenness centrality, which is a good predictor of betweenness centrality, can be carried out on social network data sets with millions of nodes.
17 citations
TL;DR: Two classical power indices, Banzhaf and Shapley-Shubik, and two new measures, effort and satisfaction, related to the spread of influence process that emerge from the subjacent influence game are proposed.
Abstract: We propose as centrality measures for social networks two classical power indices, Banzhaf and Shapley-Shubik, and two new measures, effort and satisfaction, related to the spread of influence process that emerge from the subjacent influence game. We perform a comparison of these measures with three well known centrality measures, degree, closeness and betweenness, applied to three simple social networks.
16 citations
08 Oct 2014
TL;DR: This work proposes a resampling-based framework to estimate the approximation error defined as the difference between the true and the estimated values of the centrality, and experimentally evaluates the fundamental performance of the proposed framework.
Abstract: We address a problem of efficiently estimating value of a centrality measure for a node in a large social network only using a partial network generated by sampling nodes from the entire network. To this end, we propose a resampling-based framework to estimate the approximation error defined as the difference between the true and the estimated values of the centrality. We experimentally evaluate the fundamental performance of the proposed framework using the closeness and betweenness centralities on three real world networks, and show that it allows us to estimate the approximation error more tightly and more precisely with the confidence level of 95% even for a small partial network compared with the standard error traditionally used, and that we could potentially identify top nodes and possibly rank them in a given centrality measure with high confidence level only from a small partial network.
16 citations
TL;DR: For a random directed and undirected graph the distribution or point- and graph-centrality based on degree is derived and the behaviour of graph centrality for different network sizes and densities is analysed.
16 citations
TL;DR: Graph Fourier Transform Centrality (GFT-C) is introduced, a metric that incorporates local as well as global characteristics of a node, to quantify the importance of a nodes in a complex network.
Abstract: Identifying central nodes is very crucial to design efficient communication networks or to recognize key individuals of a social network. In this paper, we introduce Graph Fourier Transform Centrality (GFT-C), a metric that incorporates local as well as global characteristics of a node, to quantify the importance of a node in a complex network. GFT-C of a reference node in a network is estimated from the GFT coefficients derived from the importance signal of the reference node. Our study reveals the superiority of GFT-C over traditional centralities such as degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and Google PageRank centrality, in the context of various arbitrary and real-world networks with different degree–degree correlations.
16 citations