DSpace Repository

Epidemic Spreading in Signed SIIS Model

Show simple item record

dc.contributor.author Shamil, Amina
dc.contributor.author Dadlani, Aresh
dc.date.accessioned 2020-05-18T04:22:39Z
dc.date.available 2020-05-18T04:22:39Z
dc.date.issued 2020-05
dc.identifier.uri http://nur.nu.edu.kz/handle/123456789/4721
dc.description.abstract The emergence of link polarity in social networks has opened up many research grounds in recent years. One direction that has received immense attention is the spreading of dynamic processes over signed networks. Having broad applications in economics, brand advertising, and social networking, research in this area is yet in its infancy. This capstone is devoted to the investigation of the interplay between opposing opinions/ideas in the presence of positive (friendly) and negative (unfriendly) social relationships in online networks. Grounded in Heider’s structural balance theory, the presented work is an improvement that accounts for the impact of both, user state and connection state on the prevalence of the spreading process. Moreover, our results are based on simulations conducted using online social network data sets that were extracted and analyzed. en_US
dc.language.iso en en_US
dc.publisher Nazarbayev University School of Engineering and Digital Sciences en_US
dc.rights Attribution-NonCommercial-ShareAlike 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/us/ *
dc.subject Social Network en_US
dc.subject Epidemic spreading en_US
dc.subject Balance theory en_US
dc.subject Signed Network en_US
dc.subject Research Subject Categories::TECHNOLOGY en_US
dc.title Epidemic Spreading in Signed SIIS Model en_US
dc.type Capstone Project en_US
workflow.import.source science


Files in this item

The following license files are associated with this item:

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-ShareAlike 3.0 United States Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 3.0 United States