scholarly journals Betweenness centrality measures for directed graphs

1994 ◽  
Vol 16 (4) ◽  
pp. 335-346 ◽  
Author(s):  
Douglas R. White ◽  
Stephen P. Borgatti
Keyword(s):  
Betweenness Centrality ◽  
Directed Graphs ◽  
Centrality Measures

scholarly journals Exact and Approximate Algorithms for Computing Betweenness Centrality in Directed Graphs

2021 ◽  
Vol 182 (3) ◽  
pp. 219-242
Author(s):  
Mostafa Haghir Chehreghani ◽  
Albert Bifet ◽  
Talel Abdessalem
Keyword(s):  
Betweenness Centrality ◽  
Directed Graphs ◽  
Randomized Algorithm ◽  
Exact Algorithm ◽  
Directed Network ◽  
Weighted Graphs ◽  
Single Vertex ◽  
Small Constant ◽  
Positive Weights ◽  
Real World Datasets

Graphs (networks) are an important tool to model data in different domains. Realworld graphs are usually directed, where the edges have a direction and they are not symmetric. Betweenness centrality is an important index widely used to analyze networks. In this paper, first given a directed network G and a vertex r ∈ V (G), we propose an exact algorithm to compute betweenness score of r. Our algorithm pre-computes a set ℛ𝒱(r), which is used to prune a huge amount of computations that do not contribute to the betweenness score of r. Time complexity of our algorithm depends on |ℛ𝒱(r)| and it is respectively Θ(|ℛ𝒱(r)| · |E(G)|) and Θ(|ℛ𝒱(r)| · |E(G)| + |ℛ𝒱(r)| · |V(G)| log |V(G)|) for unweighted graphs and weighted graphs with positive weights. |ℛ𝒱(r)| is bounded from above by |V(G)| – 1 and in most cases, it is a small constant. Then, for the cases where ℛ𝒱(r) is large, we present a simple randomized algorithm that samples from ℛ𝒱(r) and performs computations for only the sampled elements. We show that this algorithm provides an (ɛ, δ)-approximation to the betweenness score of r. Finally, we perform extensive experiments over several real-world datasets from different domains for several randomly chosen vertices as well as for the vertices with the highest betweenness scores. Our experiments reveal that for estimating betweenness score of a single vertex, our algorithm significantly outperforms the most efficient existing randomized algorithms, in terms of both running time and accuracy. Our experiments also reveal that our algorithm improves the existing algorithms when someone is interested in computing betweenness values of the vertices in a set whose cardinality is very small.

scholarly journals What makes a board director better connected? Evidence from graph theory

2020 ◽  
Vol 17 (2) ◽  
pp. 357-377
Author(s):  
Laleh Samarbakhsh ◽  
Boza Tasic
Keyword(s):  
Betweenness Centrality ◽  
Board Member ◽  
Centrality Measures ◽  
Board Members ◽  
Degree Centrality ◽  
Gender And Education ◽  
Board Directors ◽  
Board Director ◽  
High Level ◽  
The Impact

We are interested in quantifying and uncovering the relationships that form between the board directors of companies. Using these relationships we compute three network centrality measures for each director in the network and employ them in the analysis of connectedness of directors. Our focus in this study is on the attributes that make a board member better connected. The biological, educational and experiential attributes are used as independent variables to develop a regression model measuring the impact on the three connectivity measures (degree, betweenness and closeness). Our results show that ?Age? has a direct significant impact on all connectedness measures of a board member. We also find that female directors have a higher measure of degree centrality and betweenness centrality, but lower closeness. The number of foreign degrees increases the degree centrality and betweenness centrality but not closeness. The three identified characteristics of ?Age?, ?Gender?, and ?Education? are supporting the idea that a high level of social connection can in part be expected by the characteristics of individual board members and can explain up to 25% of the board member?s connectivity.

scholarly journals Two betweenness centrality measures based on Randomized Shortest Paths

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Ilkka Kivimäki ◽  
Bertrand Lebichot ◽  
Jari Saramäki ◽  
Marco Saerens
Keyword(s):  
Betweenness Centrality ◽  
Shortest Paths ◽  
Centrality Measures

scholarly journals Betweenness Centrality in Some Classes of Graphs

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Sunil Kumar Raghavan Unnithan ◽  
Balakrishnan Kannan ◽  
Madambi Jathavedan
Keyword(s):  
Real World ◽  
Betweenness Centrality ◽  
Shortest Paths ◽  
Centrality Measures ◽  
Flow Of Information

There are several centrality measures that have been introduced and studied for real-world networks. They account for the different vertex characteristics that permit them to be ranked in order of importance in the network. Betweenness centrality is a measure of the influence of a vertex over the flow of information between every pair of vertices under the assumption that information primarily flows over the shortest paths between them. In this paper we present betweenness centrality of some important classes of graphs.

scholarly journals Interlocking directorates and different power forms: An explorative analysis in the Italian context

2018 ◽  
Vol 14 (2) ◽  
pp. 7-19 ◽  
Author(s):  
Salvatore Esposito De Falco ◽  
Nicola Cucari ◽  
Federica Di Franco
Keyword(s):  
Betweenness Centrality ◽  
Power Structure ◽  
Centrality Measures ◽  
Network Density ◽  
Weighted Sum ◽  
Eigenvector Centrality ◽  
Interlocking Directorates ◽  
The Social ◽  
Italian Context ◽  
Cross Shareholdings

The purpose of the present paper is twofold. The first is to update the contribution by Drago et al. (2011) about cross-shareholdings and interlocking directorates in Italian listed companies (FTSE MIB) to 31 December 2016 and to reinforce theory of enlarged collusion. The second is to find how interlocking directorates can contribute to understanding the power structure. By using the social network analysis, we map the network structure of interlocking boards and employ centrality measures like degree, eigenvector and betweenness centrality along with the network density and average degree. We interpret eigenvector centrality as a measure of “effective power” of the connections because it can be seen as a weighted sum of not only direct connections but indirect connections, while betweenness centrality as a measure of “potential power” because it is a proxy of the volume of information that passes through the nodes. In this way, we provide a framework for selecting Italian firms with effective and potential power – around whom interactions and processes can be traced and analysed. In addition, we find that the position assumed by the controlling group of the Mediobanca Galaxy is definitely downsized.

A domain-specific measure of centrality for water distribution networks

2019 ◽  
Vol 27 (2) ◽  
pp. 341-355 ◽  
Author(s):  
Seyed Ashkan Zarghami ◽  
Indra Gunawan
Keyword(s):  
Complex Networks ◽  
Betweenness Centrality ◽  
Water Distribution ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Centrality Measures ◽  
Content Type ◽  
Centrality Measure ◽  
Centrality Analysis ◽  
Practical Implications

Purpose In recent years, centrality measures have been extensively used to analyze real-world complex networks. Water distribution networks (WDNs), as a good example of complex networks, exhibit properties not shared by other networks. This raises concerns about the effectiveness of applying the classical centrality measures to these networks. The purpose of this paper is to generate a new centrality measure in order to stick more closely to WDNs features. Design/methodology/approach This work refines the traditional betweenness centrality by adding a hydraulic-based weighting factor in order to improve its fit with the WDNs features. Rather than an exclusive focus on the network topology, as does the betweenness centrality, the new centrality measure reflects the importance of each node by taking into account its topological location, its demand value and the demand distribution of other nodes in the network. Findings Comparative analysis proves that the new centrality measure yields information that cannot be captured by closeness, betweenness and eigenvector centrality and is more accurate at ranking the importance of the nodes in WDNs. Practical implications The following practical implications emerge from the centrality analysis proposed in this work. First, the maintenance strategy driven by the new centrality analysis enables practitioners to prioritize the components in the network based on the priority ranking attributed to each node. This allows for least cost decisions to be made for implementing the preventive maintenance strategies. Second, the output of the centrality analysis proposed herein assists water utilities in identifying the effects of components failure on the network performance, which in turn can support the design and deployment of an effective risk management strategy. Originality/value The new centrality measure, proposed herein, is distinct from the conventional centrality measures. In contrast to the classical centrality metrics in which the importance of components is assessed based on a pure topological viewpoint, the proposed centrality measure integrates both topological and hydraulic attributes of WDNs and therefore is more accurate at ranking the importance of the nodes.

Detection of Wrinkle Frames in Endoluminal Videos Using Betweenness Centrality Measures for Images

2014 ◽  
Vol 18 (6) ◽  
pp. 1831-1838 ◽  
Author(s):  
Santi Segui ◽  
Michal Drozdzal ◽  
Ekaterina Zaytseva ◽  
Carolina Malagelada ◽  
Fernando Azpiroz ◽  
...  
Keyword(s):  
Betweenness Centrality ◽  
Centrality Measures
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