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Best influential spreaders identification using network global structural properties.
Namtirtha, Amrita; Dutta, Animesh; Dutta, Biswanath; Sundararajan, Amritha; Simmhan, Yogesh.
Afiliação
  • Namtirtha A; Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, 560012, India. namtirtha.asansol@gmail.com.
  • Dutta A; Department of Computer Science and Engineering, National Institute of Technology Durgapur, Durgapur, West Bengal, 713209, India.
  • Dutta B; Documentation Research and Training Centre (DRTC), Indian Statistical Institute, Bangalore, 560059, India.
  • Sundararajan A; Dept of Applied Mathematics and Computational Sciences, PSG College of Technology, Coimbatore, 641004, India.
  • Simmhan Y; Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, 560012, India.
Sci Rep ; 11(1): 2254, 2021 01 26.
Article em En | MEDLINE | ID: mdl-33500445
Influential spreaders are the crucial nodes in a complex network that can act as a controller or a maximizer of a spreading process. For example, we can control the virus propagation in an epidemiological network by controlling the behavior of such influential nodes, and amplify the information propagation in a social network by using them as a maximizer. Many indexing methods have been proposed in the literature to identify the influential spreaders in a network. Nevertheless, we have notice that each individual network holds different connectivity structures that we classify as complete, incomplete, or in-between based on their components and density. These affect the accuracy of existing indexing methods in the identification of the best influential spreaders. Thus, no single indexing strategy is sufficient from all varieties of network connectivity structures. This article proposes a new indexing method Network Global Structure-based Centrality (ngsc) which intelligently combines existing kshell and sum of neighbors' degree methods with knowledge of the network's global structural properties, such as the giant component, average degree, and percolation threshold. The experimental results show that our proposed method yields a better spreading performance of the seed spreaders over a large variety of network connectivity structures, and correlates well with ranking based on an SIR model used as ground truth. It also out-performs contemporary techniques and is competitive with more sophisticated approaches that are computationally cost.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article