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13
A Faster Algorithm for Betweenness Centrality
 Journal of Mathematical Sociology
, 2001
"... The betweenness centrality index is essential in the analysis of social networks, but costly to compute. Currently, the fastest known algorithms require #(n ) time and #(n ) space, where n is the number of actors in the network. ..."
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Cited by 291 (5 self)
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The betweenness centrality index is essential in the analysis of social networks, but costly to compute. Currently, the fastest known algorithms require #(n ) time and #(n ) space, where n is the number of actors in the network.
Centrality and Network Flow
"... Centrality measures, or at least our interpretations of these measures, make implicit assumptions about the manner in which things flow through a network. For example, some measures count only geodesic paths, apparently assuming that whatever flows through the network only moves along the shortest p ..."
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Cited by 58 (1 self)
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Centrality measures, or at least our interpretations of these measures, make implicit assumptions about the manner in which things flow through a network. For example, some measures count only geodesic paths, apparently assuming that whatever flows through the network only moves along the shortest possible paths. This paper lays out a typology of network flows based on two dimensions of variation, namely, the kinds of trajectories that traffic may follow (geodesics, paths, trails or walks), and the method of spread (broadcast, serial replication, or transfer). Measures of centrality are then matched to the kinds of flows they are appropriate for. Simulations are used to examine the relationship between type of flow and the differential importance of nodes with respect to key measurements such as speed of reception of traffic and frequency of receiving traffic. It is shown that the offtheshelf formulas for centrality measures are fully applicable only for the specific flow processes they are designed for, and that when they are applied to other flow processes they get the “wrong” answer. It is noted that the most commonly used centrality measures are not appropriate for most of the flows we are routinely interested in. A key claim made in this paper is that centrality measures can be regarded as generating expected values for certain kinds of node outcomes (such as speed and frequency of reception) given implicit models of how things flow.
Distribution of power in exchange networks: Theory and experimental results
 American Journal of Sociology
, 1983
"... you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, noncommercial use. Please contact the publisher regarding any further use of this work. Publisher contact inform ..."
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Cited by 52 (3 self)
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you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, noncommercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at
Fast Approximation of Centrality
 Journal of Graph Algorithms and Applications
, 2001
"... Social studies researchers use graphs to model group activities in social networks. An important property in this context is the centrality of a vertex: the inverse of the average distance to each other vertex. We describe a randomized approximation algorithm for centrality in weighted graphs. For g ..."
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Cited by 32 (0 self)
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Social studies researchers use graphs to model group activities in social networks. An important property in this context is the centrality of a vertex: the inverse of the average distance to each other vertex. We describe a randomized approximation algorithm for centrality in weighted graphs. For graphs exhibiting the small world phenomenon, our method estimates the centrality of all vertices with high probability within a (1 + #) factor in nearlinear time. 1 Introduction In social network analysis, the vertices of a graph represent agents in a group and the edges represent relationships, such as communication or friendship. The idea of applying graph theory to analyze the connection between the structural centrality and group process was introduced by Bavelas [4]. Various measurement of centrality [7, 14, 15] have been proposed for analyzing communication activity, control, or independence within a social network. We are particularly interested in closeness centrality [5, 6, 24]...
Intellectual property rights, strategic technology agreements and market structure: the case of GSM
 Research Policy
, 2002
"... Economics, Law and Intellectual Property ’ (June 2000) and participants at a seminar at ECIS for helpful comments and discussion. This paper investigates the role of intellectual property rights (IPRs) in shaping the GSM industry. This industry is an example of a hightech industry in which standard ..."
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Cited by 11 (1 self)
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Economics, Law and Intellectual Property ’ (June 2000) and participants at a seminar at ECIS for helpful comments and discussion. This paper investigates the role of intellectual property rights (IPRs) in shaping the GSM industry. This industry is an example of a hightech industry in which standards play a large role. In the process of designing the GSM standard, a lot of attention has been given to IPRs, mainly to avoid a situation in which a single IPR holder could hamper or even totally block the development of the standard. Nevertheless, the ultimate GSM standard contains a large amount of socalled ‘essential IPRs’, i.e., IPRs without which the implementation of GSM products is impossible. The paper starts with a general discussion of the development of GSM, and the role of firm strategy and IPRs in this process. Next, we present a database on the essential IPRs in the GSM standard. This database has been compiled on the basis of international patent statistics, and the data that manufacturers have supplied to ETSI, the European standardization body responsible for defining the GSM standard. We use this database to assess the dynamic IPR position of firms in the original GSM standard and its subsequent development.
Faster Evaluation of ShortestPath Based Centrality Indices
, 2000
"... Centrality indices are an important tool in network analysis, and many of them are derived from the set of all shortest paths of the underlying graph. The socalled betweenness centrality index is essential for the analysis of social networks, but most costly to compute. Currently, the fastest known ..."
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Cited by 1 (0 self)
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Centrality indices are an important tool in network analysis, and many of them are derived from the set of all shortest paths of the underlying graph. The socalled betweenness centrality index is essential for the analysis of social networks, but most costly to compute. Currently, the fastest known algorithms require Theta(n³) time and Theta(n²) space, where n is the number of vertices. Motivated by the fastgrowing need to compute centrality indices on large, yet very sparse, networks, new algorithms for betweenness are introduced in this paper. They require O(n + m) space and run in O(n(m + n)) or O(n(m + n log n)) time on unweighted or weighted graphs, respectively, where m is the number of edges. Since these algorithms simply augment singlesource shortestpaths computations, all standard centrality indices based on shortest paths can now be computed uniformly in one framework. Experimental evidence is provided that this substantially increases the range of network...
BioMed Central
, 2009
"... Subjective versus objective risk in genetic counseling for hereditary breast and/or ovarian cancers ..."
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Subjective versus objective risk in genetic counseling for hereditary breast and/or ovarian cancers
Predicting Hierarchical Structure in Small World Social Networks
"... Abstract—Typical analytical measures in graph theory like degree centrality, betweenness and closeness centralities are very common and have long history of their successful use. However, modeling of covert, terrorist or criminal networks through social graphs do not really provide the hierarchical ..."
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Abstract—Typical analytical measures in graph theory like degree centrality, betweenness and closeness centralities are very common and have long history of their successful use. However, modeling of covert, terrorist or criminal networks through social graphs do not really provide the hierarchical structure of such networks because these networks are composed of leaders and followers. In this short paper we investigate small world networks by computing first the Bayes posteriori probability which is then used to calculate the entropy of the network. The computed probability and entropy distribution further utilized in predicting the command structure of the network.
Locating Key Actors in Social Networks Using Bayes ’ Posterior Probability Framework
"... Abstract. Typical analytical measures in graph theory like degree centrality, betweenness and closeness centralities are very common and have long history of their successful use. However, modeling of covert, terrorist or criminal networks through social graph dose not really provide the hierarchica ..."
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Abstract. Typical analytical measures in graph theory like degree centrality, betweenness and closeness centralities are very common and have long history of their successful use. However, modeling of covert, terrorist or criminal networks through social graph dose not really provide the hierarchical structure of such networks because these networks are composed of leaders and followers. It is possible mathematically, for some graphs to estimate the probability that the removal of a certain number of nodes would split the networks into may be non functional network. In this research we investigate and analyze a social network using Bayes probability theory model to calculate entropy of each node present in the network to high light the important actors in the network. This is accomplished by observing the amount of entropy change computed by successively removing each node in the network.
From Assortative to Dissortative Networks: . . .
, 2010
"... We consider a dynamic model of network formation where agents form and sever links based on the centrality of their potential partners. We show that the existence of capacity constrains in the amount of links an agent can maintain introduces a transition from dissortative to assortative networks. Th ..."
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We consider a dynamic model of network formation where agents form and sever links based on the centrality of their potential partners. We show that the existence of capacity constrains in the amount of links an agent can maintain introduces a transition from dissortative to assortative networks. This effect can shed light on the distinction between technological and social networks as it gives a simple mechanism explaining how and why this transition occurs.