Article

The dynamics of viral marketing

Authors:

Lada A. Adamic,

Bernardo A. HubermanAuthors Info & Claims

EC '06: Proceedings of the 7th ACM conference on Electronic commerce

Pages 228 - 237

https://doi.org/10.1145/1134707.1134732

Published: 11 June 2006 Publication History

Abstract

We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We then establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a model that successfully identifies product and pricing categories for which viral marketing seems to be very effective.

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Cited By

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
Li BHao DChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Core-Competitiveness in Partially Observable Networked MarketProceedings of the ACM Web Conference 202410.1145/3589334.3645555(156-166)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645555
Li BHao DZhao D(2024)Diffusion auction design with transaction costsAutonomous Agents and Multi-Agent Systems10.1007/s10458-023-09631-838:1Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10458-023-09631-8
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Index Terms

The dynamics of viral marketing
1. Applied computing
  1. Law, social and behavioral sciences
    1. Economics

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Published In

cover image ACM Conferences

EC '06: Proceedings of the 7th ACM conference on Electronic commerce

June 2006

342 pages

ISBN:1595932364

DOI:10.1145/1134707

General Chair:
Joan Feigenbaum
Yale University, USA
,
Program Chairs:
John Chuang
University of California Berkeley, USA
,
David M. Pennock
Yahoo! Research, USA

Copyright © 2006 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: 11 June 2006

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EC06

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EC06: ACM Conference on Electronic Commerce

June 11 - 15, 2006

Michigan, Ann Arbor, USA

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Overall Acceptance Rate 664 of 2,389 submissions, 28%

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EC '25

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The 25th ACM Conference on Economics and Computation

July 7 - 11, 2025

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Cited By

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
Li BHao DChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Core-Competitiveness in Partially Observable Networked MarketProceedings of the ACM Web Conference 202410.1145/3589334.3645555(156-166)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645555
Li BHao DZhao D(2024)Diffusion auction design with transaction costsAutonomous Agents and Multi-Agent Systems10.1007/s10458-023-09631-838:1Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10458-023-09631-8
Li BHao D(2024)A Summary of Core-Competitiveness in Partially Observable Networked MarketMechanism Design in Social Networks10.1007/978-981-96-0214-8_5(65-75)Online publication date: 22-Dec-2024
https://doi.org/10.1007/978-981-96-0214-8_5
Zheng JGe XLi BZhao D(2024)Sybil-Proof Mechanism for Information Propagation with BudgetsMechanism Design in Social Networks10.1007/978-981-96-0214-8_1(1-18)Online publication date: 22-Dec-2024
https://doi.org/10.1007/978-981-96-0214-8_1
Zeng LFan CChen C(2023)Leveraging Minimum Nodes for Optimum Key Player Identification in Complex Networks: A Deep Reinforcement Learning Strategy with Structured Reward ShapingMathematics10.3390/math1117369011:17(3690)Online publication date: 28-Aug-2023
https://doi.org/10.3390/math11173690
Saurabh SZehavi M(2023)Parameterized complexity of multi-node hubsJournal of Computer and System Sciences10.1016/j.jcss.2022.08.001131(64-85)Online publication date: Feb-2023
https://doi.org/10.1016/j.jcss.2022.08.001
Hajarathaiah KEnduri MAnamalamudi SSangi A(2023)Algorithms for Finding Influential People with Mixed Centrality in Social NetworksArabian Journal for Science and Engineering10.1007/s13369-023-07619-w48:8(10417-10428)Online publication date: 6-Feb-2023
https://doi.org/10.1007/s13369-023-07619-w
Wallinger MArchambault DAuber DNöllenburg MPeltonen J(2022)Edge-Path Bundling: A Less Ambiguous Edge Bundling ApproachIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.311479528:1(313-323)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1109/TVCG.2021.3114795
Xiao GLong ZChen YQin YWu F(2022)Row-Segmented Sparse-Dense Matrix Matrix Multiplication on GPUs2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta)10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00074(376-383)Online publication date: Dec-2022
https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00074
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