Influence maximisation in social networks. (2019)

• Record Type: Journal Article
• Authors: Tejaswi, V.; Bindu, P.V.; Thilagam, P. Santhi
• Is Part Of: International journal of computational science and engineering. Volume 18:Number 2(2019)
• Page Start: 103
• Page End: 117
• Publication Date: 2019
• Subjects: social networks -- social network analysis -- influence maximisation -- labelled influence maximisation -- approximation algorithms -- information diffusion -- influence propagation models -- threshold models -- cascade models; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

Abstract

Influence maximisation is one of the significant research areas in social network analysis. It helps in identifying influential entities from social networks that can be used in marketing, election campaigns, outbreak detection and so on. Influence maximisation deals with the problem of finding a subset of nodes called seeds in the social network such that these nodes will eventually spread maximum influence in the network. This is an NP-hard problem. The aim of this paper is to provide a complete understanding of the influence maximisation problem. This paper focuses on providing an overview on the influence maximisation problem, and covers three major aspects: 1) different types of inputs required; 2) influence propagation models that map the spread of influence in the network; 3) the approximation algorithms proposed for seed set selection. In addition, we provide the state of the art and describe the open problems in this domain.