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Social Network Analysis and Mining

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01.12.2020 | Original Article

Achieving spectral localization of network using betweenness-based edge perturbation

verfasst von: Debasis Mohapatra, Soubhagya Ranjan Pradhan

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2020

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Abstract

Graph is a simple but effective way to represent a complex system, where a node represents a component of the system and edge represents connection between the components. Several insights can be inferred by analyzing such graphs. In this field, optimization of spread and localization are relatively a new research domain. The objective of the spread problem is to maximize the influence, whereas localization controls the diffusion. In this paper, our focus is on the eigenvector localization of the network adjacency matrix using inverse participation ratio (IPR). In this context, we propose betweenness centrality-based perturbation (BP) to localize the network. The results show that the BP approach achieves a better localization than the existing random perturbation (RP) approach. It shows maximum IPR than RP. The performance of the approaches is evaluated using threshold rate of diffusion (τ), number of modifications and IPR. Susceptible–infected–susceptible model is used to investigate the τ value.

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Titel: Achieving spectral localization of network using betweenness-based edge perturbation
verfasst von: Debasis Mohapatra
Soubhagya Ranjan Pradhan
Publikationsdatum: 01.12.2020
Verlag: Springer Vienna
Erschienen in: Social Network Analysis and Mining / Ausgabe 1/2020
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-020-00687-y

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