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Article

Mining knowledge-sharing sites for viral marketing

Authors:

Matthew Richardson,

Pedro DomingosAuthors Info & Claims

KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 61 - 70

https://doi.org/10.1145/775047.775057

Published: 23 July 2002 Publication History

Abstract

Viral marketing takes advantage of networks of influence among customers to inexpensively achieve large changes in behavior. Our research seeks to put it on a firmer footing by mining these networks from data, building probabilistic models of them, and using these models to choose the best viral marketing plan. Knowledge-sharing sites, where customers review products and advise each other, are a fertile source for this type of data mining. In this paper we extend our previous techniques, achieving a large reduction in computational cost, and apply them to data from a knowledge-sharing site. We optimize the amount of marketing funds spent on each customer, rather than just making a binary decision on whether to market to him. We take into account the fact that knowledge of the network is partial, and that gathering that knowledge can itself have a cost. Our results show the robustness and utility of our approach.

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Mining knowledge-sharing sites for viral marketing

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Information

Published In

cover image ACM Conferences

KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining

July 2002

719 pages

ISBN:158113567X

DOI:10.1145/775047

Conference Chair:
Osmar R. Zaïane
University of Alberta, Canada
,
General Chair:
Randy Goebel
University of Alberta, Canada
,
Program Chairs:
David Hand
Imperial College, UK
,
Daniel Keim
AT&T
,
Raymond Ng
University of British Columbia, Canada

Copyright © 2002 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 23 July 2002

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KDD02

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KDD02: The Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

July 23 - 26, 2002

Alberta, Edmonton, Canada

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KDD '02 Paper Acceptance Rate 44 of 307 submissions, 14%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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ARASAWA KMATSUKAWA SSUGIO NTAKAHARA MHATTORI S(2025)Bookmarking Forecast and its Factor Analysis of Social Network Users Based on Gradient Boosting Decision Tree勾配ブースティング決定木に基づくソーシャルネットワーク利用者のお気に入り登録予測およびその要因分析Transactions of Japan Society of Kansei Engineering10.5057/jjske.TJSKE-D-24-0001824:1(99-109)Online publication date: 2025
https://doi.org/10.5057/jjske.TJSKE-D-24-00018
Xujin CXiaodong HYingying SChenhao WXiaoying W(2025)The equilibrium existence of strategic influence games in social networksSCIENTIA SINICA Mathematica10.1360/SSM-2024-014855:2(199)Online publication date: 3-Jan-2025
https://doi.org/10.1360/SSM-2024-0148
Li MWang ZLi KLiao X(2025)Multiagent-System-Based Attention Mechanism for Predicting Product Popularity: Handling Positive–Negative Diffusion Over Social NetworksIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.333010036:1(1925-1932)Online publication date: Jan-2025
https://doi.org/10.1109/TNNLS.2023.3330100
Singh SSrivastava DVerma MMuhuri S(2025)Information diffusion analysis: process, model, deployment, and applicationThe Knowledge Engineering Review10.1017/S026988892400010939Online publication date: 22-Jan-2025
https://doi.org/10.1017/S0269888924000109
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Ferreira VPessoa AVidal T(2025)Influence optimization in networks: New formulations and valid inequalitiesComputers & Operations Research10.1016/j.cor.2024.106857173(106857)Online publication date: Jan-2025
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Makse HZava MMakse HZava M(2025)Mathematical Theories of Influencers in Complex NetworksThe Science of Influencers and Superspreaders10.1007/978-3-031-78058-5_1(1-143)Online publication date: 19-Feb-2025
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Zehmakan AZhou XZhang ZDastani MSichman JAlechina NDignum V(2024)Viral Marketing in Social Networks with Competing ProductsProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3663069(2047-2056)Online publication date: 6-May-2024
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Agra ASamuco J(2024)A New Algorithm Framework for the Influence Maximization Problem Using Graph ClusteringInformation10.3390/info1502011215:2(112)Online publication date: 14-Feb-2024
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Han ZShi LLiu LJiang LFang JLin FZhang JPanneerselvam JAntonopoulos N(2024)A Survey on Event Tracking in Social Media Data StreamsBig Data Mining and Analytics10.26599/BDMA.2023.90200217:1(217-243)Online publication date: Mar-2024
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