Showing papers on "Katz centrality published in 2009"
TL;DR: It is found that the four centrality measures are significantly correlated with citation counts and it is suggested thatcentrality measures can be useful indicators for impact analysis.
Abstract: Many studies on coauthorship networks focus on network topology and network statistical mechanics. This article takes a different approach by studying micro-level network properties with the aim of applying centrality measures to impact analysis. Using coauthorship data from 16 journals in the field of library and information science (LIS) with a time span of 20 years (1988–2007), we construct an evolving coauthorship network and calculate four centrality measures (closeness centrality, betweenness centrality, degree centrality, and PageRank) for authors in this network. We find that the four centrality measures are significantly correlated with citation counts. We also discuss the usability of centrality measures in author ranking and suggest that centrality measures can be useful indicators for impact analysis. © 2009 Wiley Periodicals, Inc.
294 citations
28 Jun 2009
TL;DR: Both feature analysis and experimental comparative studies revealed the general profile of selected measures of centrality in the social network profile.
Abstract: Network analysis offers many centrality measures that are successfully utilized in the process of investigating the social network profile. The most important and representative measures are presented in the paper. It includes indegree centrality, proximity prestige, rank prestige, node position, outdegree centrality, eccentrality, closeness centrality, and betweenes centrality. Both feature analysis and experimental comparative studies revealed the general profile of selected measures.
83 citations
TL;DR: This paper provides an expansion for group betweenness in terms of increasingly higher orders of co-betweenness, in a manner analogous to the Taylor series expansion of a mathematical function in calculus, and demonstrates the utility of this expansion by using it to construct analytic lower and upper bounds for group aroundness.
72 citations
TL;DR: A new measure termed extensity centrality is proposed, taking into account the distribution of an author’s collaborative relationships, and the strength of collaborative ties is chosen, which is closely related to Salton's measure.
Abstract: Although there are many measures of centrality of individuals in social networks, and those centrality measures can be applied to the analysis of authors’ importance in co-authorship networks, the distribution of an author’s collaborative relationships among different communities has not been considered. This distribution or extensity is an important aspect of authors’ activity. In the present study, we will propose a new measure termed extensity centrality, taking into account the distribution of an author’s collaborative relationships. In computing the strength of collaborative ties, which is closely related to the extensity centrality, we choose Salton’s measure. We choose the ACM SIGKDD data as our testing data set, and analyze the result of authors’ importance from different points of view.
66 citations
TL;DR: In this article, the authors proposed a novel centrality measure based on the dynamical properties of a biased random walk to provide a general framework for the centrality of vertex and edge in scale-free networks (SFNs).
Abstract: We propose a novel centrality measure based on the dynamical properties of a biased random walk to provide a general framework for the centrality of vertex and edge in scale-free networks (SFNs). The suggested centrality unifies various centralities such as betweenness centrality (BC), load centrality (LC) and random walk centrality (RWC) when the degree, k, is relatively large. The relation between our centrality and other centralities in SFNs is clearly shown by both analytic and numerical methods. Regarding to the edge centrality, there have been few established studies in complex networks. Thus, we also provide a systematic analysis for the edge BC (LC) in SFNs and show that the distribution of edge BC satisfies a power-law. Furthermore we also show that the suggested centrality measures on real networks work very well as on the SFNs.
32 citations
TL;DR: In this article, an axiomatic characterization of Degree, Closeness and Decay centrality measures is provided and it is shown that these measures belong to the same family of measures.
Abstract: The social and economic networks literature commonly uses different measures to capture various notions of centrality. Given the structural features of a network, there is limited consensus on how to select the appropriate centrality measure. This paper provides an axiomatic characterization of Degree, Closeness and Decay centrality measures and argues that such axiomatizations are the correct way to distinguish between centrality measures. Further, I show that these centrality measures belong to the same family of measures. This result shows why they have been found to be correlated in empirical work.
27 citations
01 Jan 2009
TL;DR: In this article, the authors examine how establishing early centrality in company networks may predict later performance and define a way of classifying centrality trajectories in social networks, providing a method that can be used more generally to predict network change over time.
Abstract: This study examines how establishing early centrality in company networks may predict later performance. Using a simulation, we show that there are strategies that correlate with eventual centrality and profit, and other strategies that correlate with isolation and poor performance. The paper also defines a way of classifying centrality trajectories in social networks, providing a method that can be used more generally to predict network change over time.
6 citations
21 Aug 2009
5 citations
Posted Content•
TL;DR: The modularity-maximization method for community detection is extended to use this centrality metric as a measure of node connectivity to study network structure, and it is shown that it leads to better insight into network structure than earlier methods.
Abstract: Bonacich centrality measures the number of attenuated paths between nodes in a network We use this metric to study network structure, specifically, to rank nodes and find community structure of the network To this end we extend the modularity-maximization method for community detection to use this centrality metric as a measure of node connectivity Bonacich centrality contains a tunable parameter that sets the length scale of interactions By studying how rankings and discovered communities change when this parameter is varied allows us to identify globally important nodes and structures We apply the proposed method to several benchmark networks and show that it leads to better insight into network structure than earlier methods
4 citations
30 May 2009
TL;DR: It was found that the major changes in the structure of the network concern its local topology, which changes significantly which may be detected with motif analysis and visible changes in node clustering coefficients.
Abstract: Different ways of detecting structural changes in email-based social networks are presented in the paper. A social network chosen for experiments was created on the basis of the Wroclaw University of Technology email server logs covering the period of 20 months. Structural parameters like degree centrality and prestige, clustering coefficients as well as betweenness and closeness centrality were computed for each of the consecutive months and their changes were analyzed. Our aim was to make an insight into dynamics of Internet-based social networks based on email service. It was found that the major changes in the structure of the network concern its local topology. Global indices like betweenness and closeness centrality remain relatively stable which also concerns the distribution of the local parameters such as degree centrality and prestige. However, the network size and local topology changes significantly which may be detected with motif analysis and visible changes in node clustering coefficients.
3 citations
01 Jan 2009
TL;DR: A novel form of centrality : the second order centrality which can be computed in a fully decentralized manner which provides locally each node with its relative criticity and relies on a random walk visiting the network in an unbiased fashion is introduced.
Abstract: A complex network can be modeled as a graph representing the "who knows who" relationship. In the context of graph theory for social networks, the notion of centrality is used to assess the relative importance of nodes in a given network topology. For example, in a network composed of large dense clusters connected through only a few links, the nodes involved in those links are particularly critical as far as the network survivability is concerned. This may also impact any application running on top of it. Such information can be exploited for various topological maintenance issues to prevent congestion and disruptance. This can also be used offline to identify the most important nodes in large social interaction graphs. Several forms of centrality have been proposed so far. Yet, they suffer from imperfections : designed for abstract graphs, they are either of limited use (degree centrality), either uncomputable in a distributed setting (random walk betweenness centrality). In this paper we introduce a novel form of centrality : the second order centrality which can be computed in a fully decentralized manner. This provides locally each node with its relative criticity and relies on a random walk visiting the network in an unbiased fashion. To this end, each node records the time elapsed between visits of that random walk (called return time in the sequel) and computes the standard deviation (or second order moment) of such return times. Both through theoretical analysis and simulation, we show that the standard deviation can be used to accurately identify critical nodes as well as to globally characterize graphs topology in a fully decentralized way.
01 Feb 2009
TL;DR: In this article, the authors generalized the betweenness definition in Bounded Budget Betweenness Centrality Game (called B3C game) introduced in [1] to only count shortest paths with a length limit.
Abstract: In this technical report, we generalize the betweenness definition in Bounded Budget Betweenness Centrality Game (called B3C game) introduced in [1] to only count shortest paths with a length limit ` We denote this game `-B3C game We prove that the hardness results in [1] about nonuniform game still hold in this generalized version In Section 2, we provide the detailed definition of the `-B3C game In Section 3, we prove that there exists an instance of `-B3C game such that it does not have any maximal Nash equilibrium In Section 4, we prove that it is NP-hard to decide whether an instance of `-B3C game has a maximal or strict Nash equilibrium
01 Jan 2009
TL;DR: A new measure quantifying the distance of nodes to the network center called weighted distance to nearest center (WDNC) is introduced, based on edge-weighted closeness (EWC), a weighted version of closeness.
Abstract: In Social Network Analysis (SNA) centrality measures focus on activity (degree), information access (betweenness), distance to all the nodes (closeness), or popularity (pagerank). We introduce a new measure quantifying the distance of nodes to the network center. It is called weighted distance to nearest center (WDNC) and it is based on edge-weighted closeness (EWC), a weighted version of closeness. It combines elements of weighted centrality as well as clustering. The WDNC will be tested on two e-mail networks of the R community, one of the most important open source programs for statistical computing and graphics. We will find that there is a relationship between the WDNC and the formal organization of the R community.