research-article

Optimizing budget allocation among channels and influencers

Authors:

Moshe TennenholtzAuthors Info & Claims

WWW '12: Proceedings of the 21st international conference on World Wide Web

Pages 381 - 388

https://doi.org/10.1145/2187836.2187888

Published: 16 April 2012 Publication History

Abstract

Brands and agencies use marketing as a tool to influence customers. One of the major decisions in a marketing plan deals with the allocation of a given budget among media channels in order to maximize the impact on a set of potential customers. A similar situation occurs in a social network, where a marketing budget needs to be distributed among a set of potential influencers in a way that provides high-impact.

We introduce several probabilistic models to capture the above scenarios. The common setting of these models consists of a bipartite graph of source and target nodes. The objective is to allocate a fixed budget among the source nodes to maximize the expected number of influenced target nodes. The concrete way in which source nodes influence target nodes depends on the underlying model. We primarily consider two models: a source-side influence model, in which a source node that is allocated a budget of k makes k independent trials to influence each of its neighboring target nodes, and a target-side influence model, in which a target node becomes influenced according to a specified rule that depends on the overall budget allocated to its neighbors. Our main results are an optimal (1-1/e)-approximation algorithm for the source-side model, and several inapproximability results for the target-side model, establishing that influence maximization in the latter model is provably harder.

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Index Terms

Optimizing budget allocation among channels and influencers
1. Theory of computation
  1. Design and analysis of algorithms

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Published In

cover image ACM Other conferences

WWW '12: Proceedings of the 21st international conference on World Wide Web

April 2012

1078 pages

ISBN:9781450312295

DOI:10.1145/2187836

General Chairs:
Alain Mille
Université de Lyon, France
,
Fabien Gandon
INRIA, France
,
Jacques Misselis
HP, France
,
Program Chairs:
Michael Rabinovich
Case Western Reserve University, USA
,
Steffen Staab
University of Koblenz-Landau, Germany

Copyright © 2012 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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Univ. de Lyon: Universite de Lyon

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 April 2012

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WWW 2012

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Univ. de Lyon

WWW 2012: 21st World Wide Web Conference 2012

April 16 - 20, 2012

Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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https://doi.org/10.1109/TKDE.2024.3523472
Ye LChi MLiu ZWang XGupta V(2025)Online mixed discrete and continuous optimization: Algorithms, regret analysis and applicationsAutomatica10.1016/j.automatica.2025.112189175(112189)Online publication date: May-2025
https://doi.org/10.1016/j.automatica.2025.112189
Stoica ANastl VHardt MSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Causal inference from competing treatmentsProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693969(46657-46691)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693969
Wang HTang BLiu CMao SZhou JDai ZSun YXie QWang XWang D(2024)HiBid: A Cross-Channel Constrained Bidding System With Budget Allocation by Hierarchical Offline Deep Reinforcement LearningIEEE Transactions on Computers10.1109/TC.2023.334311173:3(815-828)Online publication date: Mar-2024
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Zhou GLiu BQiang Y(2024)Deletion-Robust Submodular Maximization Under the Cardinality Constraint over the Integer LatticeComputational Data and Social Networks10.1007/978-981-97-0669-3_22(234-242)Online publication date: 29-Feb-2024
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Iacob ACautis BManiu SSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Sequential Learning Algorithms for Contextual Model-Free Influence MaximizationProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599498(822-831)Online publication date: 6-Aug-2023
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Chen SDu DYang WGao S(2023)Maximizing the Ratio of Monotone DR-Submodular Functions on Integer LatticeJournal of the Operations Research Society of China10.1007/s40305-023-00469-113:1(142-160)Online publication date: 27-Mar-2023
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Chen SChen WDai YYuan JZhang H(2023)Efficient presolving methods for the influence maximization problemNetworks10.1002/net.2216182:3(229-253)Online publication date: 4-Jul-2023
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