Holistic Influence Maximization for Targeted Advertisements in Spatial Social Networks

Paper


Li, Jianxin, Cai, Taotao, Mian, Ajmal, Li, Rong-Hua, Sellis, Timos and Yu, Jeffrey Xu. 2018. "Holistic Influence Maximization for Targeted Advertisements in Spatial Social Networks ." 2018 IEEE 34th International Conference on Data Engineering (ICDE). Paris, France 16 - 19 Apr 2018 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/ICDE.2018.00145
Paper/Presentation Title

Holistic Influence Maximization for Targeted Advertisements in Spatial Social Networks

Presentation TypePaper
AuthorsLi, Jianxin, Cai, Taotao, Mian, Ajmal, Li, Rong-Hua, Sellis, Timos and Yu, Jeffrey Xu
Journal or Proceedings TitleProceedings of 2018 IEEE 34th International Conference on Data Engineering (ICDE)
Journal Citationpp. 1340-1343
Number of Pages4
Year2018
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISBN9781538655207
Digital Object Identifier (DOI)https://doi.org/10.1109/ICDE.2018.00145
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/document/8509366
Web Address (URL) of Conference Proceedingshttps://ieeexplore.ieee.org/xpl/conhome/8476188/proceeding
Conference/Event2018 IEEE 34th International Conference on Data Engineering (ICDE)
Event Details
2018 IEEE 34th International Conference on Data Engineering (ICDE)
Parent
International Conference on Data Engineering
Delivery
In person
Event Date
16 to end of 19 Apr 2018
Event Location
Paris, France
AbstractThe problem of influence maximization has recently received significant attention. However, most studies focused on user influence via cyber interactions while ignoring their physical interactions which are important to gauge influence propagation. Additionally, targeted campaigns or advertisements have not received sufficient attention. To do this, we first devise a novel holistic influence diffusion model and then formulate a new holistic influence maximization query problem and develop three algorithms. Finally, we conduct extensive experiments to evaluate the effectiveness and efficiency of the proposed solutions.
KeywordsSpatial Social Networks
Related Output
Is supplemented byhttps://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/DP160102114
Is supplemented byhttps://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/DP190102443
Is supplemented byhttps://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/LP180100750
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460501. Data engineering and data science
Public NotesFiles associated with this item cannot be displayed due to copyright restrictions, but may be accessed online. Please see the link in the URL field.
Byline AffiliationsUniversity of Western Australia
Beijing Institute of Technology, China
Swinburne University of Technology
Chinese University of Hong Kong, China
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