Top-K influential users selection based on combined Katz centrality and propagation probability

Abstract

The influence maximization has attracted a lot of interest and attention in recent decades due to its various application such as advertising and spot suspicious users that may paralyze the functionality of a certain system and rumor control. The main purpose of this paper is to select influential users depending on the available budget that maximizes the influence coverage in the network. Existing work, mainly focus on designing methods based on centrality metrics due to their low time complexity and acceptable influence spread and since approaches based greedy algorithm suffer from high time complexity. In this paper, we propose a new algorithm “PrKatz” based on Katz centrality and a propagation probability threshold that provides a certain ability to influence users' successfully. The experimental results on large datasets demonstrate the performance of our proposed algorithm compared with the state of the art algorithms in term of influence spread.