Holistic influence maximization for targeted advertisements in spatial social networks

Jianxin Li, Taotao Cai, Ajmal Mian, Rong Hua Li, Timos Sellis, Jeffrey Xu Yu

Research output: Chapter in Book/Conference paperConference paper

5 Citations (Scopus)

Abstract

The 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.

Original languageEnglish
Title of host publicationProceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1344-1347
Number of pages4
ISBN (Electronic)9781538655207
DOIs
Publication statusPublished - 24 Oct 2018
Event34th IEEE International Conference on Data Engineering, ICDE 2018 - Paris, France
Duration: 16 Apr 201819 Apr 2018

Conference

Conference34th IEEE International Conference on Data Engineering, ICDE 2018
CountryFrance
CityParis
Period16/04/1819/04/18

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  • Cite this

    Li, J., Cai, T., Mian, A., Li, R. H., Sellis, T., & Yu, J. X. (2018). Holistic influence maximization for targeted advertisements in spatial social networks. In Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018 (pp. 1344-1347). [8509366] USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICDE.2018.00145