Social network mining can be divided into two categories, namely, the study of structural characteristics and content analysis.One of the most significant problem in the context of a social network is finding the most influential entities within the network.This task has significance in viral marketing, since the most influential entities can be targeted for endorsing new products in the market.However, the problem of discovering the most persuasive node in a social network has proved to be NP-hard and also the exact algorithms cannot be designed.
This creates a wide scope for developing approximation methods and algorithms popularfilm.blog that are able to produce solutions with proven approximation guarantees.Greedy algorithm serves as a base for most of the existing algorithms designed for dealing with these problems.Greedy algorithm can achieve a good approximation, but it is found to ealisboa.com be computationally expensive.Therefore, in this paper we propose a two level approach, designed based on Suspected-Infected (SI) epidemic model for maximizing the influence spread.
We further propose that, multithreading approach for implementation of algorithm for the proposed SI model aids to further elevate the performance of proposed algorithm in terms of influence spread per second.Keywords: Influencers, Social network analysis, Diffusion model, SI model, Multithreading, Marketing strategies.