Estimating the degree centrality ranking

Saxena, Akrati; Malik, Vaibhav; Iyengar, S. R. S.

doi:10.1109/comsnets.2016.7440022

Cited by 21 publications

(13 citation statements)

References 29 publications

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“…Two criteria have been proposed to generate an adjacency matrix from a similarity matrix, such as the fixed amount of link density (Agarwal et al, 2018b(Agarwal et al, , 2019 or global fixed thresholds (Jha et al, 2015;Sivakumar and Woldemeskel, 2014). However, both criteria are subjective and may lead to the presence of weak and nonsignificant links in the complex network.…”

Section: Network Constructionmentioning

confidence: 99%

Optimal design of hydrometric station networks based on complex network analysis

Agarwal

Marwan

Rathinasamy

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. Hydrometric networks play a vital role in providing information for decision-making in water resource management. They should be set up optimally to provide as much information as possible that is as accurate as possible and, at the same time, be cost-effective. Although the design of hydrometric networks is a well-identified problem in hydrometeorology and has received considerable attention, there is still scope for further advancement. In this study, we use complex network analysis, defined as a collection of nodes interconnected by links, to propose a new measure that identifies critical nodes of station networks. The approach can support the design and redesign of hydrometric station networks. The science of complex networks is a relatively young field and has gained significant momentum over the last few years in different areas such as brain networks, social networks, technological networks, or climate networks. The identification of influential nodes in complex networks is an important field of research. We propose a new node-ranking measure – the weighted degree–betweenness (WDB) measure – to evaluate the importance of nodes in a network. It is compared to previously proposed measures used on synthetic sample networks and then applied to a real-world rain gauge network comprising 1229 stations across Germany to demonstrate its applicability. The proposed measure is evaluated using the decline rate of the network efficiency and the kriging error. The results suggest that WDB effectively quantifies the importance of rain gauges, although the benefits of the method need to be investigated in more detail.

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Section: Network Constructionmentioning

confidence: 99%

Optimal design of hydrometric station networks based on complex network analysis

Agarwal

Marwan

Rathinasamy

et al. 2020

Hydrol. Earth Syst. Sci.

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“…If we purely take the links held by the node into consideration, the importance of node can be denoted by degree centrality. Degree centrality (DC) [21,22] is a typical method based on local information, and it holds that the influence of a node is reduced to the number of its neighbor nodes. In a social network, a node represents a person, an edge represents the friendship between them, so DC believes that the person with more friends is more important.…”

Section: Related Workmentioning

confidence: 99%

A novel indicator weight tuning method based on fuzzy theory in mobile social networks

Gao¹,

Xu²,

Wang³

et al. 2020

Preprint

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Mobile social network supports mobile communication and asynchronous social networking. How to measure the importance of nodes is critical and this problem remains to be answered. Most of the existing methods are subjective, so how to determine the weights of the centrality indicators is the key to solve the problem. In this paper, 9 common centrality indicators are viewed as our research object. We introduce fuzzy theory to partition the indicator weights, and to be more specific, we define a membership degree function to get the initial weight interval. With relative entropy, the weights of each centrality indicator can be obtained. By calculating the random data generated by simulation, genetic algorithm with single point crossover is used to optimize the weight of each indicator. Experiments show that the optimized weights are more effective and differentiable.

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“…Identifying nodes that occupy interesting positions in a real-world network using node ranking helps to extract meaningful information from large datasets with little cost. Usually, the measures degree or betweenness centrality are used for node ranking (Gao et al, 2013;Okamoto et al, 2008;Saxena et al, 2016). However, these measures have certain disadvantages which are explained using a simple network, the undirected and unweighted network = ( , ) with 8 nodes and 11 edges 5…”

Section: Comparison With Existing Node Ranking Measures Using Synthetmentioning

confidence: 99%

Optimal Design of Hydrometric Station Networks Based on Complex Network Analysis

Agarwal¹,

Marwan²,

Rathinasamy³

et al. 2018

Preprint

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AbstractsHydrometric networks play a vital role in providing information for decision-making in water resources management. They should be set up optimally to provide as much and as accurate information as possible, and at the same time, be cost-effective.We propose a new measure, based on complex network analysis, to support the design and redesign of hydrometric station 15 networks. The science of complex networks is a relatively young field and has gained significant momentum in the last years in different areas such as brain networks, social networks, technological networks or climate networks. The identification of influential nodes in complex networks is an important field of research. We propose a new node ranking measure, the weighted degree-betweenness, to evaluate the importance of nodes in a network. It is compared to previously proposed measures on synthetic sample networks and then applied to a real-world rain gauge network comprising 1229 stations across Germany to 20 check its applicability in the optimal design of hydrometric networks. The proposed measure is evaluated using the decline rate of network efficiency and the kriging error. The results suggest that it effectively quantifies the importance of rain stations.The new measure is very useful in identifying influential stations which need high attention and expendable stations which can be removed without much loss of information provided by the station network.

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Estimating the degree centrality ranking

Cited by 21 publications

References 29 publications

Optimal design of hydrometric station networks based on complex network analysis

Optimal design of hydrometric station networks based on complex network analysis

A novel indicator weight tuning method based on fuzzy theory in mobile social networks

Optimal Design of Hydrometric Station Networks Based on Complex Network Analysis

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