Grassroots VS elites: Which ones are better candidates for influence maximization in social networks?

Dong Li, Wei Wang, Jiming LIU*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

How to select a set of seed users under a limited budget from the social networks to maximize information/influence diffusion is a critical task in the social computing filed, called as influence maximization (IM) problem. Existing studies usually seek users with high influence (“elites”) as seeds. Each selected elite user can take a large influence increment, however, s/he also consumes a high cost (e.g. money). In the time of Web 2.0, ordinary users (“grassroots”) become the main body of the internet and online society, instead of elites. Therefore, we consider whether allocating proportionally the cost of one elite to several grassroots to promote information diffusion can achieve a greater diffusion performance. Following this mind, we propose an alternative solution for the IM problem that attempts to select ordinary grassroots as seeds in this paper. Specifically, we first empirically prove that grassroots are better choices than elites in the IM problem from the aspects of relationship strengths and polarities, based on statistics and analysis of real datasets. Next, we develop a grassroots-oriented seed users seeking algorithm which fully explores the community information of the network structure. Comprehensive experiments on Epinions and Slashdot demonstrate the effectiveness and efficiency of our method.

Original languageEnglish
Pages (from-to)321-331
Number of pages11
JournalNeurocomputing
Volume358
DOIs
Publication statusPublished - 17 Sep 2019

Scopus Subject Areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

User-Defined Keywords

  • Community
  • Grassroots
  • Influence maximization
  • Polarity
  • Strength

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