Document Type

Article

Publication Date

5-30-2017

Publication Title

Journal of Big Data

Volume

4

Issue

1

First page number:

1

Last page number:

19

Abstract

Network theory concepts form the core of algorithms that are designed to uncover valuable insights from various datasets. Especially, network centrality measures such as Eigenvector centrality, Katz centrality, PageRank centrality etc., are used in retrieving top-K viral information propagators in social networks,while web page ranking in efficient information retrieval, etc. In this paper, we propose a novel method for identifying top-K viral information propagators from a reduced search space. Our algorithm computes the Katz centrality and Local average centrality values of each node and tests the values against two threshold (constraints) values. Only those nodes, which satisfy these constraints, form the search space for top-K propagators. Our proposed algorithm is tested against four datasets and the results show that the proposed algorithm is capable of reducing the number of nodes in search space at least by 70%. We also considered the parameter (α and β) dependency of Katz centrality values in our experiments and established a relationship between the α values, number of nodes in search space and network characteristics. Later, we compare the top-K results of our approach against the top-K results of degree centrality. © 2017, The Author(s).

File Format

PDF

File Size

1572 KB

Language

english

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IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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