Katz centrality

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

Correlating centralities of social networks

Abstract: Centrality is an important measure to identify the most important actors in a network. This paper discusses the various Centrality Measures used in Social Network Analysis. These measures are tested on complex real-world social network data sets such as Video Sharing Networks, Social Interaction Network and Co-Authorship Networks to examine their effects on them. We carry out the correlation analysis of these centralities and plot the results to recommend when to use those centrality measures. Additionally, we introduce a new centrality measure — Cohesion Centrality based on the cohesiveness of a graph, develop its sequential algorithm and further devise a parallel algorithm to implement it.

Controlling the katz-bonacich centrality in social network: application to gossip in online social networks

Abstract: Recent papers studied the control of spectral centrality measures of a network by manipulating the topology of the network. We extend these works by focusing on a specific spectral centrality measure, the Katz-Bonacich centrality. The optimization of the Katz-Bonacich centrality using a topological control is called the Katz-Bonacich optimization problem. We first prove that this problem is equivalent to a linear optimization problem. Thus, in the context of large graphs, we can use state of the art algorithms. We provide a specific applications of the Katz-Bonacich centrality minimization problem based on the minimization of gossip propagation and make some experiments on real networks.

Temporal Centrality Prediction in Opportunistic Mobile Social Networks

Abstract: In this paper, we focus on predicting nodes' future importance under three important metrics, namely betweenness, and closeness centrality, using real mobility traces in Opportunistic Mobile Social Networks OMSNs. Through real trace-driven simulations, we find that nodes' importance is highly predictable due to natural social behaviour of human. Then, based on the observations in the simulation, we design several reasonable prediction methods to predict nodes' future temporal centrality. Finally, extensive real trace-driven simulations are conducted to evaluate the performance of our proposed methods. The results show that the Recent Uniform Average method performs best when predicting the future Betweenness centrality, and the Periodical Average Method performs best when predicting the future Closeness centrality in the MIT Reality trace. Moreover, the Recent Uniform Average method performs best in the Infocom 06 trace.

Network competition - the co-existence of hub-and-spoke and point-to-point

Abstract: Airlines network choices are analysed to describe the co-existence of alternative business models: the full service model based on the hub-and-spoke (HS) system and the low cost model based on point-to-point (PP) system. The analysis is carried on both theoretically and empirically. In the theoretical part, we show that the rise of the low costs business model can be the consequence of a simple two-player game. When two carriers compete in designing their network configurations (HS or PP), asymmetric equilibria emerge, i.e. one carrier will choose HS and the other PP. Full service carriers are stuck to a HS configuration to serve intercontinental destinations, whilst non-flag carriers implement a point-to-point network. In the second part, the recent network evolution in Europe is empirically evaluated by means of different spatial measures of concentration, such as Gini index, Freeman centrality index and Bonacich centrality. In addition, we also provide an airline-specific measure of centrality based on scheduled time comparison of direct to one-stop services. Spatial measures of centrality capture a reduction of centrality in non-flag carriers and small changes in the network centrality of flag carriers. Indeed, the time-based measure of centrality suggests an increase of centrality of flag carriers.

Significance of Eigenvector Centrality for Routing in a Delay Tolerant Network

Abstract: Centrality measure is an important concept in networks. It indicates the relative importance of nodes in a network. Various centrality measures have been proposed in the literature, such as degree centrality, closeness centrality etc. Practically all these measures are some values based on the properties of the node concerned. Eigenvector centrality takes into account the centrality value of the neighbours of a node to assign a centrality value to it. In this paper, we show how this value can be utilized to select relay nodes in a delay tolerant network and improve the delivery delay.