Finding Influential Spreaders in Weighted Networks Using Weighted-Hybrid Method

Finding efficient influencers has attracted a lot of researchers considering the advantages and the various ways in which it can be used. There are a lot of methods but most of them are available for unweighted networks, while there are numerous weighted networks available in real life. Finding influential users on weighted networks has numerous applications like influence maximization, controlling rumours, etc. Many algorithms such as weighted-Degree, weighted-VoteRank, weighted-h-index, and entropy-based methods have been used to rank the nodes in a weighted network according to their spreading capability. Our proposed method can be used in case of both weighted or unweighted networks for finding strong influencers efficiently. Weighted-VoteRank and weighted-H-index methods take the local spreading capability of the nodes into account, while entropy takes both local and global capability of influencing the nodes in consideration. In this paper, we consider the advantages and drawbacks of the various methods and propose a weighted-hybrid method using our observations. First, we try to improve the performance of weighted-VoteRank and weighted-h-index methods and then propose a weighted-hybrid method, which combines the performance of our improved weighted-VoteRank, improved weighted-H-index, and entropy method. Simulations using an epidemic model, Susceptible-Infected-Recovered (SIR) model produces better results as compared to other standard methods.

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