Incremental deployment of network monitors based on Group Betweenness Centrality

Dolev, Shlomi; Elovici, Yuval; Puzis, Rami; Zilberman, Polina

doi:10.1016/j.ipl.2009.07.019

Cited by 41 publications

(44 citation statements)

References 10 publications

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“…This fact enables using combinatorial optimization methods for finding a group of given size with the maximal GBC (Puzis et al, 2007b). In the past, GBC optimization methods were successfully applied in communication networks for optimizing network traffic monitoring (Dolev et al, 2009;Puzis et al, 2009). …”

Section: Betweenness and Group Betweenness Centralitymentioning

confidence: 99%

“…A similar problem in communication networks was solved using group betweenness centrality (GBC) (Dolev et al, 2009;Everett & Borgatti, 1999;Puzis et al, 2009). Since GBC is an immediate natural extension of BC, all variants of BC described in the fifth Section are applicable to GBC.…”

Section: Optimizing the Locations Of Monitoring Stationsmentioning

confidence: 99%

“…One of the advantages of betweenness centrality is that it simultaneously considers all shortest paths between an origin and a destination. Another advantage of betweenness centrality employed in this paper is its natural extension to group betweenness centrality which was effectively applied in communication networks for optimizing the deployment of collaborative traffic monitoring systems (Dolev et al, 2009;Puzis et al, 2009). In this article we use the proposed betweenness-driven traffic assignment model in order to optimize the locations of monitoring units in transportation networks.…”

Section: Introductionmentioning

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: Betweenness and Group Betweenness Centralitymentioning

confidence: 99%

Section: Optimizing the Locations Of Monitoring Stationsmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Puzis' algorithm is a branch-andbound approach that searches the space of all possible groups. Dolev et al [6] suggest a greedy heuristic to find a group with maximum betweenness centrality and show its approximation ratio, while Fink et al [9] generalizes this to the probabilistic version of the problem, which maximizes expected group centralities. Although very related, all the above papers focus on a particular groupcentrality measure, namely betweenness centrality.…”

Section: Related Workmentioning

confidence: 99%

“…Although variants of this problem have been defined in the past [6,9], we are the first to solve it for arbitrary centrality measures and provide an efficient, constantfactor approximation algorithm for the problem.…”

mentioning

confidence: 99%

A Framework for the Evaluation and Management of Network Centrality

Ishakian

Erdős

Terzi

et al. 2012

Proceedings of the 2012 SIAM International Conference on Data Mining

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Network-analysis literature is rich in node-centrality measures that quantify the centrality of a node as a function of the (shortest) paths of the network that go through it. Existing work focuses on defining instances of such measures and designing algorithms for the specific combinatorial problems that arise for each instance. In this work, we propose a unifying definition of centrality that subsumes all path-counting based centrality definitions: e.g., stress, betweenness or paths centrality. We also define a generic algorithm for computing this generalized centrality measure for every node and every group of nodes in the network. Next, we define two optimization problems: k-Group Centrality Maximization and k-Edge Centrality Boosting. In the former, the task is to identify the subset of k nodes that have the largest group centrality. In the latter, the goal is to identify up to k edges to add to the network so that the centrality of a node is maximized. We show that both of these problems can be solved efficiently for arbitrary centrality definitions using our general framework. In a thorough experimental evaluation we show the practical utility of our framework and the efficacy of our algorithms.

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On the Rationality and Optimality of Transportation Networks Defense

Altshuler

Puzis²,

Elovici³

et al. 2015

Securing Transportation Systems

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Transportation infrastructures have recently gained increasing attention in the context of homeland security. Being both a main target for attacks as well as a method for carrying out such attacks, much effort is being allocated these days towards increasing our understanding regarding transportation networks [14]. Specifically, measuring and predicting human mobility patterns along the links of a transportation network has been of a great importance to researchers in the field, as it contains the basic information needed in order to cope with transportation related threats more efficiently. Such threats can take for example the form of a group of terrorists trying to reach their target by car, or a truck filled with chemical or radioactive material. These threats require homeland security agencies to rapidly deploy monitoring or surveillance units in key junctions, dispatch air units to central locations etc. Clearly, carrying out this mission relies on the knowledge of what are those key traffic junctions, and how to deploy the existing (and always on shortage) resources most efficiently. Hitherto, producing the transportation data required for answering these questions was done off-line and relied heavily on expensive and time consuming surveying and on-field observational methods. Network Betweenness is known to be highly correlated with network load in communication and transportation networks. In this work we show how a specially designed Betweenness Centrality measure that can be useful for optimizing locations of static or mobile monitoring equipment. Furthermore, we show that the accuracy of the estimations produced using this approach can be further enhanced when additional (pre-defined and known) properties of the network are taken into account, generating an augmented Mobility Oriented Betweenness Centrality measure. We demonstrate the efficiency of the proposed method both analytically and experimentally, using real world transportation network constructed using cellular phones data, that contains a high resolution network of the Israeli transportation system. We show that the traffic flow level that were measured using this expensive and complicated method can be accurately estimated using our proposed Augmented Betweenness technique. As a result, we can generate an efficient deployment scheme of monitoring units for this specific network, and calculate the percentage of traffic it monitors.

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Incremental deployment of network monitors based on Group Betweenness Centrality

Cited by 41 publications

References 10 publications

Augmented Betweenness Centrality for Environmentally Aware Traffic Monitoring in Transportation Networks

Augmented Betweenness Centrality for Environmentally Aware Traffic Monitoring in Transportation Networks

A Framework for the Evaluation and Management of Network Centrality

On the Rationality and Optimality of Transportation Networks Defense

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