Power in Network Structures

Aleskerov, Fuad; Meshcheryakova, Natalia; Shvydun, Sergey

doi:10.1007/978-3-319-56829-4_7

Cited by 14 publications

(2 citation statements)

References 9 publications

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“…Also, Newman (2003) provides the profound overview of centrality measures in network graphs. The advanced versions of basic centrality measures for networks are proposed in Aleskerov et al (2017Aleskerov et al ( , 2018, Aleskerov and Shvydun (2018) and Meshcheryakova and Shvydun (2017). The above mentioned basic centrality measures in application for NodeXl software are well described in Hansen et al (2009).…”

Section: Methodological Issuesmentioning

confidence: 99%

Unmanned aerial vehicles research in Scopus: an analysis and visualization of publication activity and research collaboration at the country level

Kotsemir

2019

Qual Quant

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This article proposes a global bibliometric overview of unmanned aerial vehicle (UAVs) publications in the Scopus database during 1985-2015 at the country level. The article is structured as follows. The first section provides an introduction and overview of papers on the publication and patent activity in field of UAVs. Section two describes the methodological framework of the study. Section three provides an overview of co-publication activity in UAV research. Section four is devoted to an analysis of patterns of international collaboration in field of UAV research in Scopus. Finally, section five concludes the paper by summarizing the key findings from the analysis and illustrating the implications from the analysis and offering proposals for possible further developments in the field. This study uses a wide set of bibliometric indicators and network metrics for measuring different aspects of publication activity and citation levels as well as cross-country research collaboration on UAVs. In order to illustrate country-to-country research collaboration maps NodeXL software was applied.

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Section: Methodological Issuesmentioning

confidence: 99%

Unmanned aerial vehicles research in Scopus: an analysis and visualization of publication activity and research collaboration at the country level

Kotsemir

2019

Qual Quant

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“… where and are the matrices of indirect influence of nodes from networks and . These matrices can be obtain using SRIC or LRIC modes which are discussed in Aleskerov, Meshcheryakova & Shvydun (2017) and Aleskerov, Shvydun & Meshcheryakova (2021) . A distinctive feature of these models is that they identify significant edges while insignificant edges are not taken into account.…”

Section: Graph Distance Measuresmentioning

confidence: 99%

Models of similarity in complex networks

Shvydun¹

2023

PeerJ Computer Science

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The analysis of networks describing many social, economic, technological, biological and other systems has attracted a lot of attention last decades. Since most of these complex systems evolve over time, there is a need to investigate the changes, which appear in the system, in order to assess the sustainability of the network and to identify stable periods. In the literature, there have been developed a large number of models that measure the similarity among the networks. There also exist some surveys, which consider a limited number of similarity measures and then perform their correlation analysis, discuss their properties or assess their performances on synthetic benchmarks or real networks. The aim of the article is to extend these studies. The article considers 39 graph distance measures and compares them on simple graphs, random graph models and real networks. The author also evaluates the performance of the models in order to identify which of them can be applied to large networks. The results of the study reveal some important aspects of existing similarity models and provide a better understanding of their advantages and disadvantages. The major finding of the work is that many graph similarity measures of different nature are well correlated and that some comprehensive methods are well agreed with simple models. Such information can be used for the choice of appropriate similarity measure as well as for further development of new models for similarity assessment in network structures.

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Stability and Similarity in Networks Based on Topology and Nodes Importance

Aleskerov

Shvydun

2018

Studies in Computational Intelligence

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Power in Network Structures

Cited by 14 publications

References 9 publications

Unmanned aerial vehicles research in Scopus: an analysis and visualization of publication activity and research collaboration at the country level

Unmanned aerial vehicles research in Scopus: an analysis and visualization of publication activity and research collaboration at the country level

Models of similarity in complex networks

Stability and Similarity in Networks Based on Topology and Nodes Importance

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