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Random walk closeness centrality

About: Random walk closeness centrality is a research topic. Over the lifetime, 547 publications have been published within this topic receiving 57559 citations.


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Book ChapterDOI
11 Dec 2007
TL;DR: This paper presents a novel approximation algorithm for computing betweenness centrality of a given vertex, for both weighted and unweighted graphs, based on an adaptive sampling technique that significantly reduces the number of single-source shortest path computations for vertices with high centrality.
Abstract: Betweenness is a centrality measure based on shortest paths, widely used in complex network analysis. It is computationally-expensive to exactly determine betweenness; currently the fastest-known algorithm by Brandes requires O(nm) time for unweighted graphs and O(nm + n2 log n) time for weighted graphs, where n is the number of vertices and m is the number of edges in the network. These are also the worstcase time bounds for computing the betweenness score of a single vertex. In this paper, we present a novel approximation algorithm for computing betweenness centrality of a given vertex, for both weighted and unweighted graphs. Our approximation algorithm is based on an adaptive sampling technique that significantly reduces the number of single-source shortest path computations for vertices with high centrality. We conduct an extensive experimental study on real-world graph instances, and observe that our random sampling algorithm gives very good betweenness approximations for biological networks, road networks and web crawls.

347 citations

Book ChapterDOI
29 Jun 2008
TL;DR: This paper combines existing methods on calculating exact values and approximate values of closeness centrality and presents new algorithms to rank the top-kvertices with the highest closenesscentrality.
Abstract: Closeness centrality is an important concept in social network analysis. In a graph representing a social network, closeness centrality measures how close a vertex is to all other vertices in the graph. In this paper, we combine existing methods on calculating exact values and approximate values of closeness centrality and present new algorithms to rank the top-kvertices with the highest closeness centrality. We show that under certain conditions, our algorithm is more efficient than the algorithm that calculates the closeness-centralities of all vertices.

329 citations

Journal ArticleDOI
31 Mar 2006-Chaos
TL;DR: A comprehensive study of centrality distributions over geographic networks of urban streets indicates that a spatial analysis, that is grounded not on a single centrality assessment but on a set of different centrality indices, allows an extended comprehension of the city structure.
Abstract: Centrality has revealed crucial for understanding the structural properties of complex relational networks. Centrality is also relevant for various spatial factors affecting human life and behaviors in cities. Here, we present a comprehensive study of centrality distributions over geographic networks of urban streets. Five different measures of centrality, namely degree, closeness, betweenness, straightness and information, are compared over 18 1-square-mile samples of different world cities. Samples are represented by primal geographic graphs, i.e., valued graphs defined by metric rather than topologic distance where intersections are turned into nodes and streets into edges. The spatial behavior of centrality indices over the networks is investigated graphically by means of color-coded maps. The results indicate that a spatial analysis, that we term multiple centrality assessment, grounded not on a single but on a set of different centrality indices, allows an extended comprehension of the city structure, nicely capturing the skeleton of most central routes and subareas that so much impacts on spatial cognition and on collective dynamical behaviors. Statistically, closeness, straightness and betweenness turn out to follow similar functional distribution in all cases, despite the extreme diversity of the considered cities. Conversely, information is found to be exponential in planned cities and to follow a power-law scaling in self-organized cities. Hierarchical clustering analysis, based either on the Gini coefficients of the centrality distributions, or on the correlation between different centrality measures, is able to characterize classes of cities.

304 citations

Journal ArticleDOI
TL;DR: An index for centrality satisfying the axioms is presented based on the degrees of the vertices in a given undirected graph, and it will enlarge the class of comparable graphs with respect to a centrality measure.
Abstract: .— The paper considers the concept of centrality in an undirected graph. A system of axioms and an index for centrality satisfying the axioms are presented. The index is based on the degrees of the vertices in a given undirected graph, and it will enlarge the class of comparable graphs with respect to a centrality measure.

296 citations

Journal IssueDOI
Erjia Yan1, Ying Ding1
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


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20213
20191
20188
201763
201667
201579