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Influence maximization through adaptive seeding

Influence maximization through adaptive seeding Influence Maximization through Adaptive Seeding YARON SINGER Harvard University In this monograph we survey the adaptive seeding methodology for influence maximization. Influence maximization is the challenge of spreading information effectively through influential users in a social network. In many applications, one is restricted to select influencers from a set of users who engaged with the topic being promoted, and due to the structure of social networks, these users often rank low in terms of their influence potential. An alternative approach one can consider is an adaptive method which selects users in a manner which targets their influential neighbors. The advantage of such an approach is that it leverages the friendship paradox in social networks: while users are often not influential themselves, they know someone who is. Adaptive seeding is a two-stage stochastic optimization framework, designed to leverage the friendship paradox to seed high influential nodes. This framework encompasses a rich set of algorithmic challenges at the intersection of stochastic optimization and submodular optimization. We discuss this method here, along with algorithmic approaches, the friendship paradox in random graph models, and experiments on adaptive seeding. Categories and Subject Descriptors: F.0 [Theory of Computation]: General General Terms: Algorithms; Economics; Experimentation; http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGecom Exchanges Association for Computing Machinery

Influence maximization through adaptive seeding

ACM SIGecom Exchanges , Volume 15 (1) – Sep 6, 2016

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2016 by ACM Inc.
ISSN
1551-9031
DOI
10.1145/2994501.2994503
Publisher site
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Abstract

Influence Maximization through Adaptive Seeding YARON SINGER Harvard University In this monograph we survey the adaptive seeding methodology for influence maximization. Influence maximization is the challenge of spreading information effectively through influential users in a social network. In many applications, one is restricted to select influencers from a set of users who engaged with the topic being promoted, and due to the structure of social networks, these users often rank low in terms of their influence potential. An alternative approach one can consider is an adaptive method which selects users in a manner which targets their influential neighbors. The advantage of such an approach is that it leverages the friendship paradox in social networks: while users are often not influential themselves, they know someone who is. Adaptive seeding is a two-stage stochastic optimization framework, designed to leverage the friendship paradox to seed high influential nodes. This framework encompasses a rich set of algorithmic challenges at the intersection of stochastic optimization and submodular optimization. We discuss this method here, along with algorithmic approaches, the friendship paradox in random graph models, and experiments on adaptive seeding. Categories and Subject Descriptors: F.0 [Theory of Computation]: General General Terms: Algorithms; Economics; Experimentation;

Journal

ACM SIGecom ExchangesAssociation for Computing Machinery

Published: Sep 6, 2016

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