Holistic influence maximization for targeted advertisements in spatial social networks

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

doi:10.1109/ICDE.2018.00145

Holistic influence maximization for targeted advertisements in spatial social networks

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

School of Computer Science and Technology

University of Western Australia
Swinburne University of Technology
Chinese University of Hong Kong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

19 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 language	English
Title of host publication	Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1344-1347
Number of pages	4
ISBN (Electronic)	9781538655207
DOIs	https://doi.org/10.1109/ICDE.2018.00145
Publication status	Published - 24 Oct 2018
Event	34th IEEE International Conference on Data Engineering, ICDE 2018 - Paris, France Duration: 16 Apr 2018 → 19 Apr 2018

Publication series

Name	Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018

Conference

Conference	34th IEEE International Conference on Data Engineering, ICDE 2018
Country/Territory	France
City	Paris
Period	16/04/18 → 19/04/18

Keywords

Influence Maximization
Spatial Social Networks
Targeted Advertisements

Access to Document

10.1109/ICDE.2018.00145

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). Article 8509366 (Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDE.2018.00145

Li, Jianxin ; Cai, Taotao ; Mian, Ajmal et al. / Holistic influence maximization for targeted advertisements in spatial social networks. Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1344-1347 (Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018).

@inproceedings{e2bab3d552594f4482d9f69a8932bb91,

title = "Holistic influence maximization for targeted advertisements in spatial social networks",

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.",

keywords = "Influence Maximization, Spatial Social Networks, Targeted Advertisements",

author = "Jianxin Li and Taotao Cai and Ajmal Mian and Li, \{Rong Hua\} and Timos Sellis and Yu, \{Jeffrey Xu\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 34th IEEE International Conference on Data Engineering, ICDE 2018 ; Conference date: 16-04-2018 Through 19-04-2018",

year = "2018",

month = oct,

day = "24",

doi = "10.1109/ICDE.2018.00145",

language = "English",

series = "Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1344--1347",

booktitle = "Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018",

address = "United States",

}

Li, J, Cai, T, Mian, A, Li, RH, Sellis, T & Yu, JX 2018, Holistic influence maximization for targeted advertisements in spatial social networks. in Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018., 8509366, Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1344-1347, 34th IEEE International Conference on Data Engineering, ICDE 2018, Paris, France, 16/04/18. https://doi.org/10.1109/ICDE.2018.00145

Holistic influence maximization for targeted advertisements in spatial social networks. / Li, Jianxin; Cai, Taotao; Mian, Ajmal et al.
Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1344-1347 8509366 (Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Holistic influence maximization for targeted advertisements in spatial social networks

AU - Li, Jianxin

AU - Cai, Taotao

AU - Mian, Ajmal

AU - Li, Rong Hua

AU - Sellis, Timos

AU - Yu, Jeffrey Xu

PY - 2018/10/24

Y1 - 2018/10/24

N2 - 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.

AB - 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.

KW - Influence Maximization

KW - Spatial Social Networks

KW - Targeted Advertisements

UR - https://www.scopus.com/pages/publications/85057130711

U2 - 10.1109/ICDE.2018.00145

DO - 10.1109/ICDE.2018.00145

M3 - Conference contribution

AN - SCOPUS:85057130711

T3 - Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018

SP - 1344

EP - 1347

BT - Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 34th IEEE International Conference on Data Engineering, ICDE 2018

Y2 - 16 April 2018 through 19 April 2018

ER -

Li J, Cai T, Mian A, Li RH, Sellis T, Yu JX. Holistic influence maximization for targeted advertisements in spatial social networks. In Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1344-1347. 8509366. (Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018). doi: 10.1109/ICDE.2018.00145

Holistic influence maximization for targeted advertisements in spatial social networks

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