Scheduled seeding for latent viral marketing

Alon Sela; Dmitri Goldenberg; Erez Shmueli; Irad Ben-Gal

doi:10.1109/asonam.2016.7752304

Scheduled seeding for latent viral marketing

Alon Sela, Dmitri Goldenberg, Erez Shmueli, Irad Ben-Gal

Tel Aviv University

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

5 Scopus citations

Abstract

One highly studied topic in the field of social networks is the search for influential nodes, that when seeded (i.e. infected intentionally), may infect a large portion of the network through a viral process. However, when it comes to the spread of new products, such viral processes are rather rare. Social influence is indeed an important factor when it comes to the act of adopting a new product. However, this influence is usually latent and does not trigger the purchase action by itself, it therefore requires an additional sales effort. We propose a model and a method that better fit the product adoption scenario. Our method allocates the seeding efforts not only to precise nodes but also at precise points in time, such that the product adoption rate increases. By conducting a set of empirical simulations, we show that under realistic assumptions, our method improves the product adoption rate by 25%-50%.

Original language	English
Title of host publication	Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
Editors	Ravi Kumar, James Caverlee, Hanghang Tong
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	642-643
Number of pages	2
ISBN (Electronic)	9781509028467
DOIs	https://doi.org/10.1109/asonam.2016.7752304
State	Published - 21 Nov 2016
Externally published	Yes
Event	2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 - San Francisco, United States Duration: 18 Aug 2016 → 21 Aug 2016

Publication series

Name	Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

Conference

Conference	2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
Country/Territory	United States
City	San Francisco
Period	18/08/16 → 21/08/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

Information Cascades
Information Spread
Scheduled Seeding
Social Networks
Viral Marketing

Access to Document

10.1109/asonam.2016.7752304

Cite this

Sela, A., Goldenberg, D., Shmueli, E., & Ben-Gal, I. (2016). Scheduled seeding for latent viral marketing. In R. Kumar, J. Caverlee, & H. Tong (Eds.), Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 (pp. 642-643). Article 7752304 (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/asonam.2016.7752304

Sela, Alon ; Goldenberg, Dmitri ; Shmueli, Erez et al. / Scheduled seeding for latent viral marketing. Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. editor / Ravi Kumar ; James Caverlee ; Hanghang Tong. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 642-643 (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016).

@inproceedings{2b25b87ace8c4a7c8f4fcd940cb09ffa,

title = "Scheduled seeding for latent viral marketing",

abstract = "One highly studied topic in the field of social networks is the search for influential nodes, that when seeded (i.e. infected intentionally), may infect a large portion of the network through a viral process. However, when it comes to the spread of new products, such viral processes are rather rare. Social influence is indeed an important factor when it comes to the act of adopting a new product. However, this influence is usually latent and does not trigger the purchase action by itself, it therefore requires an additional sales effort. We propose a model and a method that better fit the product adoption scenario. Our method allocates the seeding efforts not only to precise nodes but also at precise points in time, such that the product adoption rate increases. By conducting a set of empirical simulations, we show that under realistic assumptions, our method improves the product adoption rate by 25%-50%.",

keywords = "Information Cascades, Information Spread, Scheduled Seeding, Social Networks, Viral Marketing",

author = "Alon Sela and Dmitri Goldenberg and Erez Shmueli and Irad Ben-Gal",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 ; Conference date: 18-08-2016 Through 21-08-2016",

year = "2016",

month = nov,

day = "21",

doi = "10.1109/asonam.2016.7752304",

language = "אנגלית",

series = "Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016",

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

pages = "642--643",

editor = "Ravi Kumar and James Caverlee and Hanghang Tong",

booktitle = "Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016",

address = "ארצות הברית",

}

Sela, A, Goldenberg, D, Shmueli, E & Ben-Gal, I 2016, Scheduled seeding for latent viral marketing. in R Kumar, J Caverlee & H Tong (eds), Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016., 7752304, Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, Institute of Electrical and Electronics Engineers Inc., pp. 642-643, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, San Francisco, United States, 18/08/16. https://doi.org/10.1109/asonam.2016.7752304

Scheduled seeding for latent viral marketing. / Sela, Alon; Goldenberg, Dmitri; Shmueli, Erez et al.
Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. ed. / Ravi Kumar; James Caverlee; Hanghang Tong. Institute of Electrical and Electronics Engineers Inc., 2016. p. 642-643 7752304 (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016).

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

TY - GEN

T1 - Scheduled seeding for latent viral marketing

AU - Sela, Alon

AU - Goldenberg, Dmitri

AU - Shmueli, Erez

AU - Ben-Gal, Irad

PY - 2016/11/21

Y1 - 2016/11/21

N2 - One highly studied topic in the field of social networks is the search for influential nodes, that when seeded (i.e. infected intentionally), may infect a large portion of the network through a viral process. However, when it comes to the spread of new products, such viral processes are rather rare. Social influence is indeed an important factor when it comes to the act of adopting a new product. However, this influence is usually latent and does not trigger the purchase action by itself, it therefore requires an additional sales effort. We propose a model and a method that better fit the product adoption scenario. Our method allocates the seeding efforts not only to precise nodes but also at precise points in time, such that the product adoption rate increases. By conducting a set of empirical simulations, we show that under realistic assumptions, our method improves the product adoption rate by 25%-50%.

AB - One highly studied topic in the field of social networks is the search for influential nodes, that when seeded (i.e. infected intentionally), may infect a large portion of the network through a viral process. However, when it comes to the spread of new products, such viral processes are rather rare. Social influence is indeed an important factor when it comes to the act of adopting a new product. However, this influence is usually latent and does not trigger the purchase action by itself, it therefore requires an additional sales effort. We propose a model and a method that better fit the product adoption scenario. Our method allocates the seeding efforts not only to precise nodes but also at precise points in time, such that the product adoption rate increases. By conducting a set of empirical simulations, we show that under realistic assumptions, our method improves the product adoption rate by 25%-50%.

KW - Information Cascades

KW - Information Spread

KW - Scheduled Seeding

KW - Social Networks

KW - Viral Marketing

UR - http://www.scopus.com/inward/record.url?scp=85006721637&partnerID=8YFLogxK

U2 - 10.1109/asonam.2016.7752304

DO - 10.1109/asonam.2016.7752304

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:85006721637

T3 - Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

SP - 642

EP - 643

BT - Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

A2 - Kumar, Ravi

A2 - Caverlee, James

A2 - Tong, Hanghang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

Y2 - 18 August 2016 through 21 August 2016

ER -

Sela A, Goldenberg D, Shmueli E, Ben-Gal I. Scheduled seeding for latent viral marketing. In Kumar R, Caverlee J, Tong H, editors, Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 642-643. 7752304. (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016). doi: 10.1109/asonam.2016.7752304

Scheduled seeding for latent viral marketing

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this