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 (alpha and beta) dependency of Katz centrality values in our experiments and established a relationship between the alpha 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.
Highlights
With the outbreak of social networking platforms such as Facebook, Twitter, Instagram, etc., there has been a trend in big data community researches to engage themselves in social network studies, as these networks have proven to be the excellent sources of hidden information patterns [1,2,3]
We propose a novel method for identifying top-K viral information propagators from a reduced search space
Conclusion and future work With growing interest in finding the most important nodes, centrality measures have been one of the sought after methods for this purpose
Summary
With the outbreak of social networking platforms such as Facebook, Twitter, Instagram, etc., there has been a trend in big data community researches to engage themselves in social network studies, as these networks have proven to be the excellent sources of hidden information patterns [1,2,3]. Instead of sending news about an offer to all the actors in the network, a set of precisely chosen actors capable of efficiently spreading the information, can be identified as information propagators to ingest the offer message into the network. For this purpose, finding popular or most influential nodes in the network has proven to be helpful [4].
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