The Experts below are selected from a list of 53507055 Experts worldwide ranked by ideXlab platform
Rami Puzis - One of the best experts on this subject based on the ideXlab platform.
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Routing Betweenness centrality
Journal of the ACM, 2010Co-Authors: Shlomi Dolev, Yuval Elovici, Rami PuzisAbstract:Betweenness-Centrality measure is often used in social and computer communication networks to estimate the potential monitoring and control capabilities a vertex may have on data flowing in the network. In this article, we define the Routing Betweenness Centrality (RBC) measure that generalizes previously well known Betweenness measures such as the Shortest Path Betweenness, Flow Betweenness, and Traffic Load Centrality by considering network flows created by arbitrary loop-free routing strategies. We present algorithms for computing RBC of all the individual vertices in the network and algorithms for computing the RBC of a given group of vertices, where the RBC of a group of vertices represents their potential to collaboratively monitor and control data flows in the network. Two types of collaborations are considered: (i) conjunctive—the group is a sequences of vertices controlling traffic where all members of the sequence process the traffic in the order defined by the sequence and (ii) disjunctive—the group is a set of vertices controlling traffic where at least one member of the set processes the traffic. The algorithms presented in this paper also take into consideration different sampling rates of network monitors, accommodate arbitrary communication patterns between the vertices (traffic matrices), and can be applied to groups consisting of vertices and/or edges. For the cases of routing strategies that depend on both the source and the target of the message, we present algorithms with time complexity of O(n²m) where n is the number of vertices in the network and m is the number of edges in the routing tree (or the routing directed acyclic graph (DAG) for the cases of multi-path routing strategies). The time complexity can be reduced by an order of n if we assume that the routing decisions depend solely on the target of the messages. Finally, we show that a preprocessing of O(n²m) time, supports computations of RBC of sequences in O(kn) time and computations of RBC of sets in O(k³n) time, where k in the number of vertices in the sequence or the set. [ABSTRACT FROM AUTHOR]
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Fast algorithm for successive computation of group Betweenness centrality
Physical Review E - Statistical Nonlinear and Soft Matter Physics, 2007Co-Authors: Rami Puzis, Yuval Elovici, Shlomi DolevAbstract:In this paper, we propose a method for rapid computation of group Betweenness centrality whose running time (after preprocessing) does not depend on network size. The calculation of group Betweenness centrality is computationally demanding and, therefore, it is not suitable for applications that compute the centrality of many groups in order to identify new properties. Our method is based on the concept of path Betweenness centrality defined in this paper. We demonstrate how the method can be used to find the most prominent group. Then, we apply the method for epidemic control in communication networks. We also show how the method can be used to evaluate distributions of group Betweenness centrality and its correlation with group degree. The method may assist in finding further properties of complex networks and may open a wide range of research opportunities.
Junsol Kim - One of the best experts on this subject based on the ideXlab platform.
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a measure of centrality in cyclic diffusion processes walk Betweenness
PLOS ONE, 2021Co-Authors: Yoosik Youm, Byungkyu Lee, Junsol KimAbstract:Unlike many traditional measures of centrality based on paths that do not allow any repeated nodes or lines, we propose a new measure of centrality based on walks, walk-Betweenness, that allows any number of repeated nodes or lines. To illustrate the value of walk-Betweenness, we examine the transmission of syphilis in Chicago area and the diffusion of microfinance in 43 rural Indian villages. Walk-Betweenness allows us to identify hidden bridging communities in Chicago that were essential in the transmission dynamics. We also find that village leaders with high walk-Betweenness are more likely to accelerate the rate of microfinance take-up among their followers, outperforming other traditional centrality measures in regression analyses.
Shlomi Dolev - One of the best experts on this subject based on the ideXlab platform.
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Routing Betweenness centrality
Journal of the ACM, 2010Co-Authors: Shlomi Dolev, Yuval Elovici, Rami PuzisAbstract:Betweenness-Centrality measure is often used in social and computer communication networks to estimate the potential monitoring and control capabilities a vertex may have on data flowing in the network. In this article, we define the Routing Betweenness Centrality (RBC) measure that generalizes previously well known Betweenness measures such as the Shortest Path Betweenness, Flow Betweenness, and Traffic Load Centrality by considering network flows created by arbitrary loop-free routing strategies. We present algorithms for computing RBC of all the individual vertices in the network and algorithms for computing the RBC of a given group of vertices, where the RBC of a group of vertices represents their potential to collaboratively monitor and control data flows in the network. Two types of collaborations are considered: (i) conjunctive—the group is a sequences of vertices controlling traffic where all members of the sequence process the traffic in the order defined by the sequence and (ii) disjunctive—the group is a set of vertices controlling traffic where at least one member of the set processes the traffic. The algorithms presented in this paper also take into consideration different sampling rates of network monitors, accommodate arbitrary communication patterns between the vertices (traffic matrices), and can be applied to groups consisting of vertices and/or edges. For the cases of routing strategies that depend on both the source and the target of the message, we present algorithms with time complexity of O(n²m) where n is the number of vertices in the network and m is the number of edges in the routing tree (or the routing directed acyclic graph (DAG) for the cases of multi-path routing strategies). The time complexity can be reduced by an order of n if we assume that the routing decisions depend solely on the target of the messages. Finally, we show that a preprocessing of O(n²m) time, supports computations of RBC of sequences in O(kn) time and computations of RBC of sets in O(k³n) time, where k in the number of vertices in the sequence or the set. [ABSTRACT FROM AUTHOR]
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Fast algorithm for successive computation of group Betweenness centrality
Physical Review E - Statistical Nonlinear and Soft Matter Physics, 2007Co-Authors: Rami Puzis, Yuval Elovici, Shlomi DolevAbstract:In this paper, we propose a method for rapid computation of group Betweenness centrality whose running time (after preprocessing) does not depend on network size. The calculation of group Betweenness centrality is computationally demanding and, therefore, it is not suitable for applications that compute the centrality of many groups in order to identify new properties. Our method is based on the concept of path Betweenness centrality defined in this paper. We demonstrate how the method can be used to find the most prominent group. Then, we apply the method for epidemic control in communication networks. We also show how the method can be used to evaluate distributions of group Betweenness centrality and its correlation with group degree. The method may assist in finding further properties of complex networks and may open a wide range of research opportunities.
John R. Hipp - One of the best experts on this subject based on the ideXlab platform.
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Pathways: Examining Street Network Configurations, Structural Characteristics and Spatial Crime Patterns in Street Segments
Journal of Quantitative Criminology, 2020Co-Authors: Young-an Kim, John R. HippAbstract:Objectives Although theories suggest that street network configurations (pathways) are important factors for understanding the spatial patterns of crime, relatively less attention has been paid to the association between the physical configuration of the street network and the level of crime in place. Consequently, we employed the concept of Betweenness centrality in the context of the street network to empirically measure the potential foot traffic passing through a given street segment. Methods We introduce a methodological refinement by accounting for the characteristics of origin and destination of each potential trip (where travelers are from and tend to go) using residential population in origins and destinations and the number of various types of business employees in destinations. Moreover, we posit that the effect of potential foot traffic into a given street segment will be moderated by certain social environmental characteristics such as socioeconomic status of place. By using data on a sample of 300,000 street segments in the Southern California region across 130 cities, we estimate a set of negative binomial regression models including the Betweenness measures. Results Our results show that Betweenness centrality has a curvilinear relationship with violent and property crime: At lower levels, increases in Betweenness results in increased crime, yet the pattern becomes crime-reducing at higher values of the Betweenness measure. We also found that the pattern is moderated by the socioeconomic status of the street segment. Conclusions The current study highlights that there is an important relationship of the physical environment in terms of the street network configuration and crime in street segments.
Marc Barthelemy - One of the best experts on this subject based on the ideXlab platform.
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From the Betweenness centrality in street networks to structural invariants in random planar graphs
Nature Communications, 2018Co-Authors: Alec Kirkley, Marc Barthelemy, Hugo Barbosa, Gourab GhoshalAbstract:The Betweenness centrality, a path-based global measure of flow, is a static predictor of congestion and load on networks. Here we demonstrate that its statistical distribution is invariant for planar networks, that are used to model many infrastructural and biological systems. Empirical analysis of street networks from 97 cities worldwide, along with simulations of random planar graph models, indicates the observed invariance to be a consequence of a bimodal regime consisting of an underlying tree structure for high Betweenness nodes, and a low Betweenness regime corresponding to loops providing local path alternatives. Furthermore, the high Betweenness nodes display a non-trivial spatial clustering with increasing spatial correlation as a function of the edge-density. Our results suggest that the spatial distribution of Betweenness is a more accurate discriminator than its statistics for comparing static congestion patterns and its evolution across cities as demonstrated by analyzing 200 years of street data for Paris.
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group Betweenness and co Betweenness inter related notions of coalition centrality
Social Networks, 2009Co-Authors: Eric D. Kolaczyk, David B. Chua, Marc BarthelemyAbstract:Abstract Vertex Betweenness centrality is a metric that seeks to quantify a sense of the importance of a vertex in a network in terms of its ‘control’ on the flow of information along geodesic paths throughout the network. Two natural ways to extend vertex Betweenness centrality to sets of vertices are (i) in terms of geodesic paths that pass through at least one of the vertices in the set, and (ii) in terms of geodesic paths that pass through all vertices in the set. The former was introduced by Everett and Borgatti [Everett, M., Borgatti, S., 1999. The centrality of groups and classes. Journal of Mathematical Sociology 23 (3), 181–201], and called group Betweenness centrality. The latter, which we call co-Betweenness centrality here, has not been considered formally in the literature until now, to the best of our knowledge. In this paper, we show that these two notions of centrality are in fact intimately related and, furthermore, that this relationship may be exploited to obtain deeper insight into both. In particular, we provide 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. We then demonstrate the utility of this expansion by using it to construct analytic lower and upper bounds for group Betweenness that involve only simple combinations of (i) the Betweenness of individual vertices in the group, and (ii) the co-Betweenness of pairs of these vertices. Accordingly, we argue that the latter quantity, i.e., pairwise co-Betweenness, is itself a fundamental quantity of some independent interest, and we present a computationally efficient algorithm for its calculation, which extends the algorithm of Brandes [Brandes, U., 2001. A faster algorithm for Betweenness centrality. Journal of Mathematical Sociology 25, 163] in a natural manner. Applications are provided throughout, using a handful of different communication networks, which serve to illustrate the way in which our mathematical contributions allow for insight to be gained into the interaction of network structure, coalitions, and information flow in social networks.
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Group Betweenness and co-Betweenness: Inter-related notions of coalition centrality ☆
Social Networks, 2009Co-Authors: Eric D. Kolaczyk, David B. Chua, Marc BarthelemyAbstract:Abstract Vertex Betweenness centrality is a metric that seeks to quantify a sense of the importance of a vertex in a network in terms of its ‘control’ on the flow of information along geodesic paths throughout the network. Two natural ways to extend vertex Betweenness centrality to sets of vertices are (i) in terms of geodesic paths that pass through at least one of the vertices in the set, and (ii) in terms of geodesic paths that pass through all vertices in the set. The former was introduced by Everett and Borgatti [Everett, M., Borgatti, S., 1999. The centrality of groups and classes. Journal of Mathematical Sociology 23 (3), 181–201], and called group Betweenness centrality. The latter, which we call co-Betweenness centrality here, has not been considered formally in the literature until now, to the best of our knowledge. In this paper, we show that these two notions of centrality are in fact intimately related and, furthermore, that this relationship may be exploited to obtain deeper insight into both. In particular, we provide 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. We then demonstrate the utility of this expansion by using it to construct analytic lower and upper bounds for group Betweenness that involve only simple combinations of (i) the Betweenness of individual vertices in the group, and (ii) the co-Betweenness of pairs of these vertices. Accordingly, we argue that the latter quantity, i.e., pairwise co-Betweenness, is itself a fundamental quantity of some independent interest, and we present a computationally efficient algorithm for its calculation, which extends the algorithm of Brandes [Brandes, U., 2001. A faster algorithm for Betweenness centrality. Journal of Mathematical Sociology 25, 163] in a natural manner. Applications are provided throughout, using a handful of different communication networks, which serve to illustrate the way in which our mathematical contributions allow for insight to be gained into the interaction of network structure, coalitions, and information flow in social networks.