Fast algorithm for successive computation of group betweenness centrality

Puzis, Rami; Elovici, Yuval; Dolev, Shlomi

doi:10.1103/physreve.76.056709

Cited by 66 publications

(70 citation statements)

References 33 publications

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“…For example, after the traffic assignment process is complete, it is difficult to reproduce the portion of the traffic flow shared by two arbitrary links. This problem can be efficiently tackled by employing the data structure maintained for efficient calculation of group betweenness centrality (Puzis et al, 2007a).…”

Section: Traffic Assignmentmentioning

confidence: 99%

“…GBC can be efficiently computed using the algorithm presented in (Puzis et al, 2007a time complexity of computing GBC of a single group (M) is O(|M| 3 ). Note that the time required to compute GBC of a single group does not depend on the size of the network.…”

Section: Betweenness and Group Betweenness Centralitymentioning

confidence: 99%

“…In contrast, to monitor location optimization using traffic assignment, the existing GBC algorithm is able to evaluate a single group (M) in time that scales as O(|M| 3 ) (see Puzis et al, 2007a). It is therefore preferable to use GBC within optimization algorithms that require evaluation of a large number of relatively small groups.…”

Section: Optimizing the Locations Of Monitoring Stationsmentioning

confidence: 99%

See 2 more Smart Citations

Augmented Betweenness Centrality for Environmentally Aware Traffic Monitoring in Transportation Networks

Puzis

Altshuler

Elovici³

et al. 2012

Journal of Intelligent Transportation Systems

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Section: Traffic Assignmentmentioning

confidence: 99%

Section: Betweenness and Group Betweenness Centralitymentioning

confidence: 99%

Section: Optimizing the Locations Of Monitoring Stationsmentioning

confidence: 99%

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Augmented Betweenness Centrality for Environmentally Aware Traffic Monitoring in Transportation Networks

Puzis

Altshuler

Elovici³

et al. 2012

Journal of Intelligent Transportation Systems

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“…The two most known measures of this type are the flow-betweenness centrality [3,6,11] and the random walk betweenness centrality [6,19]. Moreover, there are two important generalizations of the betweenness centrality, namely group betweenness [9,21,22] which identifies groups of vertices which have collective influence in a network, as well as routing betweenness [8] which considers network flows created by arbitrary loop-free routing strategies.…”

Section: Introductionmentioning

confidence: 99%

A measure for a balanced workload and its extremal values

Govorčin¹,

Škrekovski

Vukašinović

et al. 2016

Discrete Applied Mathematics

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a b s t r a c tIn order to measure the extent to which the distribution of workload between actors in the network can be equalized, a degree-weighted measure for a balanced workload based on betweenness centrality is introduced. The goal of this study is to determine the extremal values of the introduced measure, as well as the graph structures where the extremal values are attained. Several real world networks were used for evaluation of the new invariant. The obtained results are used for statistical comparison with standard measures of centrality to demonstrate validity of the introduced measure.

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“…(2) While Q is not empty: Brandes (2008). A different algorithm that efficiently computes the GBC of many groups on the same network was proposed by Puzis et al (2007b).…”

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confidence: 99%

Collaborative attack on Internet users' anonymity

et al. 2009

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Purpose -The purpose of this paper is to model and study the effectiveness of an attack on the anonymity of Internet users by a group of collaborating eavesdroppers. Design/methodology/approach -The paper is based on an analysis of the Internet topology. The study is based on two methods for choosing nodes that contribute the most to the detection of as many communicating Internet users as possible. Findings -The paper illustrates that it is possible to compromise the anonymity of many Internet users when eavesdropping on a relatively small number of nodes, even when the most central ones are protected from eavesdropping. Research limitations/implications -It is assumed that the Internet users under attack are not using any anonymity enhancing technologies, but nodes can be protected from eavesdropping. It proposes a measure of the success of an attack on Internet users' anonymity, for a given deployment of collaborating eavesdroppers in the Internet. Practical implications -The paper shows that several, and not necessarily the most prominent, collaborating nodes can compromise the anonymity of a considerable portion of Internet users. This study also emphasizes that when trying to completely compromise the anonymity of Internet users, an eavesdroppers' deployment strategy that considers eavesdroppers' collaboration can result in substantial resource saving compared to choosing a set of the most prominent nodes. Originality/value -The paper proposes a new measure of anonymity level in the network, based on the linkability of the Internet users. This paper is the first to present results of a non-trivial Group Betweenness optimization strategy in large complex networks.

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Fast algorithm for successive computation of group betweenness centrality

Cited by 66 publications

References 33 publications

Augmented Betweenness Centrality for Environmentally Aware Traffic Monitoring in Transportation Networks

Augmented Betweenness Centrality for Environmentally Aware Traffic Monitoring in Transportation Networks

A measure for a balanced workload and its extremal values

Collaborative attack on Internet users' anonymity

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