Betweenness Centrality Metrics for Assessing Electrical Power Network Robustness against Fragmentation and Node Failure

2012 ◽  
Author(s):  
Ken A. Hawick
Keyword(s):  
Betweenness Centrality ◽  
Electrical Power ◽  
Network Robustness ◽  
Power Network ◽  
Node Failure ◽  
Centrality Metrics

scholarly journals Prospective Smart Distribution Substation in Bangladesh: Modeling and Analysis

2021 ◽  
Vol 13 (19) ◽  
pp. 10904
Author(s):  
Abdul Hasib Siddique ◽  
Mehedi Hasan ◽  
Sharnali Islam ◽  
Khalid Rashid
Keyword(s):  
Distributed Generation ◽  
Fossil Fuel ◽  
New Technology ◽  
Electrical Power ◽  
Electrical Network ◽  
Power Network ◽  
Major Focus ◽  
Modeling And Analysis ◽  
Generation Structure ◽  
Smart Substation

Being one of the fastest-growing economies in the world, Bangladesh needs to upgrade its electrical network and aim to reduce dependency on fossil fuel-based energy. For the aging and ever-expanding power network, it is necessary to have a smart substation in order to provide reliable, affordable, and sustainable electrical power. As Bangladesh is looking to integrate Distributed Generation (DG) in the power system, it is high time to think about integrating a smart distribution substation into its power network. In this paper, an investigation of the current power generation structure of Bangladesh was conducted and is described. The major focus was given to the upgradation of the existing substation and distribution setup of Bangladesh by providing suitable architectures, technologies, and communication protocols. Detailed studies of Bangladesh’s prospects to incorporate the new technology and renewable energy into its power network are discussed. ETAP was used to simulate the prospective system to show the feasibility of the prospective smart distribution substation in Bangladesh’s power network.

Centrality Analysis of the United States Network Graph

2018 ◽  
pp. 1746-1756
Author(s):  
Natarajan Meghanathan
Keyword(s):  
United States ◽  
Betweenness Centrality ◽  
The United States ◽  
Power Law Distribution ◽  
Network Graph ◽  
Central State ◽  
Undirected Network ◽  
Centrality Metrics ◽  
The Us ◽  
Centrality Analysis

We model the contiguous states (48 states and the District of Columbia) of the United States (US) as an undirected network graph with each state represented as a node and there is an edge between two nodes if the corresponding two states share a common border. We determine a ranking of the states in the US with respect to the four commonly studied centrality metrics: degree, eigenvector, betweenness and closeness. We observe the states of Missouri and Maine to be respectively the most central state and the least central state with respect to all the four centrality metrics. The degree distribution is bi-modal Poisson. The eigenvector and closeness centralities also exhibit Poisson distribution, while the betweenness centrality exhibits power-law distribution. We observe a higher correlation in the ranking of vertices based on the degree centrality and betweenness centrality.

Use of Centrality Metrics for Ranking of Courses Based on Their Relative Contribution in a Curriculum Network Graph

2018 ◽  
pp. 129-159
Keyword(s):  
Betweenness Centrality ◽  
Directed Acyclic Graph ◽  
Degree Centrality ◽  
Weighted Sum ◽  
Eigenvector Centrality ◽  
Network Graph ◽  
Curriculum Assessment ◽  
Acyclic Graph ◽  
Relative Contribution ◽  
Centrality Metrics

The author proposes a centrality and topological sort-based formulation to quantify the relative contribution of courses in a curriculum network graph (CNG), a directed acyclic graph, comprising of the courses (as vertices), and their pre-requisites (captured as directed edges). The centrality metrics considered are out-degree and in-degree centrality along with betweenness centrality and eigenvector centrality. The author normalizes the values obtained for each centrality metric as well as the level numbers of the vertices in a topological sort of the CNG. The contribution score for a vertex is the weighted sum of the normalized values for the vertex. The author observes the betweenness centrality of the vertices (courses) to have the largest influence in the relative contribution scores of the courses that could be used as a measure of the weights to be given to the courses for curriculum assessment and student ranking as well as to cluster courses with similar contribution.

Computationally Light vs. Computationally Heavy Centrality Metrics

2018 ◽  
pp. 34-65
Keyword(s):  
Betweenness Centrality ◽  
Clustering Coefficient ◽  
Degree Centrality ◽  
Strongly Correlated ◽  
Eigenvector Centrality ◽  
Correlation Measure ◽  
Centrality Metrics ◽  
Two Measures ◽  
Local Clustering Coefficient ◽  
Spearman’S Correlation

In this chapter, the authors analyze the correlation between the computationally light degree centrality (DEG) and local clustering coefficient complement-based degree centrality (LCC'DC) metrics vs. the computationally heavy betweenness centrality (BWC), eigenvector centrality (EVC), and closeness centrality (CLC) metrics. Likewise, they also analyze the correlation between the computationally light complement of neighborhood overlap (NOVER') and the computationally heavy edge betweenness centrality (EBWC) metric. The authors analyze the correlations at three different levels: pair-wise (Kendall's correlation measure), network-wide (Spearman's correlation measure), and linear regression-based prediction (Pearson's correlation measure). With regards to the node centrality metrics, they observe LCC'DC-BWC to be the most strongly correlated at all the three levels of correlation. For the edge centrality metrics, the authors observe EBWC-NOVER' to be strongly correlated with respect to the Spearman's correlation measure, but not with respect to the other two measures.

scholarly journals Interdependency and Vulnerability of Multipartite Networks under Target Node Attacks

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Qing Cai ◽  
Mahardhika Pratama ◽  
Sameer Alam
Keyword(s):  
Complex Networks ◽  
Robustness Analysis ◽  
Network Models ◽  
Continuous Phase ◽  
Structure Design ◽  
Network Robustness ◽  
Multilayer Networks ◽  
Component Theory ◽  
Centrality Metrics ◽  
Entire Network

Complex networks in reality may suffer from target attacks which can trigger the breakdown of the entire network. It is therefore pivotal to evaluate the extent to which a network could withstand perturbations. The research on network robustness has proven as a potent instrument towards that purpose. The last two decades have witnessed the enthusiasm on the studies of network robustness. However, existing studies on network robustness mainly focus on multilayer networks while little attention is paid to multipartite networks which are an indispensable part of complex networks. In this study, we investigate the robustness of multipartite networks under intentional node attacks. We develop two network models based on the largest connected component theory to depict the cascading failures on multipartite networks under target attacks. We then investigate the robustness of computer-generated multipartite networks with respect to eight node centrality metrics. We discover that the robustness of multipartite networks could display either discontinuous or continuous phase transitions. Interestingly, we discover that larger number of partite sets of a multipartite network could increase its robustness which is opposite to the phenomenon observed on multilayer networks. Our findings shed new lights on the robust structure design of complex systems. We finally present useful discussions on the applications of existing percolation theories that are well studied for network robustness analysis to multipartite networks. We show that existing percolation theories are not amenable to multipartite networks. Percolation on multipartite networks still deserves in-depth efforts.

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