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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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Proceedings ArticleDOI
TL;DR: The multi-layered social network extracted from data collected from the real Web 2.0 site consists of ten distinct layers and the network analysis was performed for different degree centralities measures.
Abstract: Multi-layered social networks reflect complex relationships existing in modern interconnected IT systems. In such a network each pair of nodes may be linked by many edges that correspond to different communication or collaboration user activities. Multi-layered degree centrality for multi-layered social networks is presented in the paper. Experimental studies were carried out on data collected from the real Web 2.0 site. The multi-layered social network extracted from this data consists of ten distinct layers and the network analysis was performed for different degree centralities measures.

54 citations

Journal ArticleDOI
TL;DR: A new measure of node centrality in social networks, the Harmonic Influence Centrality (HIC), which emerges naturally in the study of social influence over networks is proposed using an intuitive analogy between social and electrical networks.
Abstract: This paper proposes a new measure of node centrality in social networks, the Harmonic Influence Centrality (HIC), which emerges naturally in the study of social influence over networks. Using an intuitive analogy between social and electrical networks, we introduce a distributed message passing algorithm to compute the HIC of each node. Although its design is based on theoretical results which assume the network to have no cycle, the algorithm can also be successfully applied on general graphs.

53 citations

Journal ArticleDOI
15 Nov 2017-Entropy
TL;DR: A novel mechanism is proposed to quantitatively measure centrality using the re-defined entropy centrality model, which is based on decompositions of a graph into subgraphs and analysis on the entropy of neighbor nodes.
Abstract: Centrality is one of the most studied concepts in network analysis. Despite an abundance of methods for measuring centrality in social networks has been proposed, each approach exclusively characterizes limited parts of what it implies for an actor to be “vital” to the network. In this paper, a novel mechanism is proposed to quantitatively measure centrality using the re-defined entropy centrality model, which is based on decompositions of a graph into subgraphs and analysis on the entropy of neighbor nodes. By design, the re-defined entropy centrality which describes associations among node pairs and captures the process of influence propagation can be interpreted explained as a measure of actor potential for communication activity. We evaluate the efficiency of the proposed model by using four real-world datasets with varied sizes and densities and three artificial networks constructed by models including Barabasi-Albert, Erdos-Renyi and Watts-Stroggatz. The four datasets are Zachary’s karate club, USAir97, Collaboration network and Email network URV respectively. Extensive experimental results prove the effectiveness of the proposed method.

53 citations

Journal ArticleDOI
TL;DR: Exogenous measures of degree, closeness and betweenness are looked at, which examine how much centrality an individual node contributes to the centrality of the other nodes in the network.

52 citations

Journal ArticleDOI
TL;DR: A family of centrality measures for directed social networks from a game theoretical point of view is defined and a characterization of the measures and an additive decomposition in three summands that can be interpreted in terms of emission, betweenness and reception centrality components.

52 citations


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