Convex combinations of centrality measures

Keng, Ying Ying; Kwa, Kiam Heong; McClain, C. J.

doi:10.1080/0022250x.2020.1765776

Cited by 8 publications

(17 citation statements)

References 27 publications

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“…However, there is no consensus on the definition of the concept of centrality. 3,4 The use of different centrality measures for different scenarios can be adopted as a method. However, in this case, information provided by one centrality measure can be obtained, whereas the information provided by other centrality measures can be missed.…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, more favorable results can be obtained when several centrality measures are used together in accordance with the structure of the given network. 4,7,8 In complex networks, it is difficult to increase accuracy without increasing time complexity in the selection of suitable nodes to start or stop the propagation process. This contradiction between accuracy and complexity has always been a problem.…”

Section: Introductionmentioning

confidence: 99%

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A new fast entropy‐based method to generate composite centrality measures in complex networks

Sabah

Şimşek

2023

Concurrency and Computation

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Summary Determining the centrality of nodes in complex networks provides practical benefits in many areas such as detecting influencer nodes, viral marketing, and preventing the spread of rumors. On the other hand, there is no consensus for the definition of centrality. Therefore, different centrality measures such as degree, closeness, and betweenness have been developed to measure the centrality of a node. However, each centrality measure highlights the various characteristics of the nodes in the network from its own point of view. This causes each centrality measure to rank the nodes in a different order. In recent years, researchers have focused on approaches that combine multiple centrality measures. Thus, the perspectives of different centrality measures can be considered simultaneously. In this study, we have proposed a fast and efficient method using the analytic hierarchy process and entropy weighting to combine multiple centrality measures. We tested the proposed method with synthetic and real datasets and compared the results with those of state‐of‐the‐art methods. The experimental results showed the proposed method to be competitive with these advanced methods, whereas it performed much better than the other methods in terms of computational speed. This indicated that our proposed method could be applied to large and dynamic complex networks.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new fast entropy‐based method to generate composite centrality measures in complex networks

Sabah

Şimşek

2023

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…However, many of the measures developed only considered the local and global impacts of the nodes [17]- [21] or network communities [18], [19], [22]- [24]. Recently, another approach has been adopted that combines multiple centrality measures to develop new hybrid centrality measures [25]- [32]. However, many of these studies ignore the dynamics of the propagation model.…”

Section: Introductionmentioning

confidence: 99%

Lexical sorting centrality to distinguish spreading abilities of nodes in complex networks under the Susceptible-Infectious-Recovered (SIR) model

Şimşek

2022

Journal of King Saud University - Computer and Information Scie

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“…More globally, efforts are underway in the network science community to use performance-oriented algorithmic techniques that allow for an efficient computation of newer node centrality measures on big network datasets (van der Grinten et al, 2020 ). Third, there is a growing interest in creating “integrative” measures that combine a basket of centrality measures (Salavaty et al, 2020 ; Keng et al, 2021 ). If analysts knew that the overall cost was driven by a few specific measures, the combination could be trimmed to remain informational while computing significantly faster.…”

Section: Introductionmentioning

confidence: 99%

An Experimental Study on the Scalability of Recent Node Centrality Metrics in Sparse Complex Networks

Freund¹,

Giabbanelli²

2022

Front. Big Data

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Node centrality measures are among the most commonly used analytical techniques for networks. They have long helped analysts to identify “important” nodes that hold power in a social context, where damages could have dire consequences for transportation applications, or who should be a focus for prevention in epidemiology. Given the ubiquity of network data, new measures have been proposed, occasionally motivated by emerging applications or by the ability to interpolate existing measures. Before analysts use these measures and interpret results, the fundamental question is: are these measures likely to complete within the time window allotted to the analysis? In this paper, we comprehensively examine how the time necessary to run 18 new measures (introduced from 2005 to 2020) scales as a function of the number of nodes in the network. Our focus is on giving analysts a simple and practical estimate for sparse networks. As the time consumption depends on the properties in the network, we nuance our analysis by considering whether the network is scale-free, small-world, or random. Our results identify that several metrics run in the order of O(nlogn) and could scale to large networks, whereas others can require O(n2) or O(n3) and may become prime targets in future works for approximation algorithms or distributed implementations.

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Convex combinations of centrality measures

Cited by 8 publications

References 27 publications

A new fast entropy‐based method to generate composite centrality measures in complex networks

A new fast entropy‐based method to generate composite centrality measures in complex networks

Lexical sorting centrality to distinguish spreading abilities of nodes in complex networks under the Susceptible-Infectious-Recovered (SIR) model

An Experimental Study on the Scalability of Recent Node Centrality Metrics in Sparse Complex Networks

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