A holistic approach to influence maximization

Nireshwalya Sumith, Basava Annappa, Swapan Bhattacharya

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

A social network is an Internet-based collaboration platform that plays a vital role in information spread, opinion-forming, trend-setting, and keeps everyone connected. Moreover, the popularity of web and social networks has interesting applications including viral marketing, recommendation systems, poll analysis, etc. In these applications, user influence plays an important role. This chapter discusses how effectively social networks can be used for information propagation in the context of viral marketing. Picking the right group of users, hoping they will cause a chain effect of marketing, is the core of viral marketing applications. The strategy used to select the correct group of users is the influence maximization problem. This chapter proposes one of the viable solutions to influence maximization. The focus is to find those users in the social networks who would adopt and propagate information, thus resulting in an effective marketing strategy. The three main components that would help in the effective spread of information in the social networks are: the network structure, the user's influence on others, and the seeding algorithm. Amalgamation of these three aspects provides a holistic solution to influence maximization.

Original languageEnglish
Title of host publicationHybrid Intelligence for Social Networks
PublisherSpringer International Publishing AG
Pages129-160
Number of pages32
ISBN (Electronic)9783319651392
ISBN (Print)9783319651385
DOIs
Publication statusPublished - 28-11-2017

All Science Journal Classification (ASJC) codes

  • General Computer Science

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