Paper Info
Title
A model of information diffusion in dynamic social networks based on evidence theory.
Abstract
Social networks currently belong to a vast area of research as information spreads at a remarkable speed due to technology, and social connections have become easily accessible in the online environment. Social networks are dynamic entities, which new individuals can join, or other links can be lost because members no longer interact with one-another. Dynamic analysis of social networks is important in topology changes of the network and also in information diffusion. Some information that spreads through the social network may be untrue, hence in this paper we propose a protocol based on evidence theory with Dempster-Shafer and Yager's rules in which the network becomes more immune to false information. We also analyze the impact of topology change for an initial network by adding new connections in the information diffusion process. We show information diffusion by coloring the nodes of the network and also illustrate the time evolution of messages for a better accuracy in our comparisons. The experimental results confirm that the proposed model fits the behavior of inhibiting false information.
Year
DOI
Venue
2019
10.3233/JIFS-179346
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
Field
DocType
Information credibility,information diffusion,social networks,confidence degree
Social network,Artificial intelligence,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
37
6.0
1064-1246
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Sabina-Adriana Floria111.04
Florin Leon27115.03
Doina Logofatu31716.74