AIM: Activation increment minimization strategy for preventing bad information diffusion in OSNs

Zhenhua Tan, Dan Ke Wu, Tianhan Gao, Ilsun You*, Vishal Sharma

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

The openness and virtuality of Online Social Networks (OSNs) make it a hotbed of rapid propagation for various kinds of frauds and erroneous information. Ergo, there is an exigent need to find a method that can expeditiously and efficaciously limit the diffusion of misinformation in OSNs. To resolve this issue, this article proposes the utilization of Activation Increment engendered by a node as a criterion to quantify the importance of the node. Even if the propagation probabilities between the nodes are identically tantamount, due to the dynamics of information propagation and high connectivity of the network, the activation probabilities of nodes are different. The Activation Increment describes the sum of activation probabilities of a node's neighbors while the node itself is in a different state (infected status, recovered status) at a certain time. To utilize Activation Increment, this paper proposes Activation Increment Minimization (AIM) strategy to select and block nodes for information diffusion. Experiments based on the real social network dataset attested that the proposed AIM strategy is superior to the traditional heuristic algorithms.

Original languageEnglish
Pages (from-to)293-301
Number of pages9
JournalFuture Generation Computer Systems
Volume94
Early online date04 Dec 2018
DOIs
Publication statusPublished - 01 May 2019
Externally publishedYes

Keywords

  • Diffusion limited
  • Information diffusion
  • Node selection strategies
  • Online social network author biographies

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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