Title
AIM: Activation increment minimization strategy for preventing bad information diffusion in OSNs.
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.
Year
DOI
Venue
2019
10.1016/j.future.2018.11.038
Future Generation Computer Systems
Keywords
Field
DocType
Diffusion limited,Node selection strategies,Information diffusion,Online social network author biographies
Heuristic,Social network,Computer science,Minification,Information propagation,Distributed computing,Virtuality (gaming)
Journal
Volume
ISSN
Citations 
94
0167-739X
1
PageRank 
References 
Authors
0.35
27
5
Name
Order
Citations
PageRank
Zhenhua Tan1133.89
Danke Wu221.75
Tianhan Gao31817.71
Ilsun You4979123.32
Vishal Sharma533534.34