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Scaling Influence Maximization with Network Abstractions

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Social Network Analysis - Community Detection and Evolution

Part of the book series: Lecture Notes in Social Networks ((LNSN))

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Abstract

Maximizing product adoption within a customer social network under a constrained advertising budget is an important special case of the general influence maximization problem. Specialized optimization techniques that account for product correlations and community effects can outperform network-based techniques that do not model interactions that arise from marketing multiple products to the same consumer base. However, it can be infeasible to use exact optimization methods that utilize expensive matrix operations on larger networks without parallel computation techniques. In this chapter, we present a hierarchical influence maximization approach for product marketing that constructs an abstraction hierarchy for scaling optimization techniques to larger networks. An exact solution is computed on smaller partitions of the network, and a candidate set of influential nodes is propagated upward to an abstract representation of the original network that maintains distance information. This process of abstraction, solution, and propagation is repeated until the resulting abstract network is small enough to be solved exactly.

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Acknowledgments

This research was supported in part by NSF IIS-08451.

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Authors and Affiliations

  1. Department of EECS, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL, 32816, USA

    Mahsa Maghami & Gita Sukthankar

Authors
  1. Mahsa Maghami
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  2. Gita Sukthankar
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Corresponding author

Correspondence to Gita Sukthankar .

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Editors and Affiliations

  1. Département d'Informatique et Ingénirie, Université du Québec en Outaouais, Gatineau, Québec, Canada

    Rokia Missaoui

  2. Département de Mathématiques et Informatique, Université Cheikh Anta Diop, Dakar, Senegal

    Idrissa Sarr

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Maghami, M., Sukthankar, G. (2014). Scaling Influence Maximization with Network Abstractions. In: Missaoui, R., Sarr, I. (eds) Social Network Analysis - Community Detection and Evolution. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-12188-8_11

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  • DOI: https://doi.org/10.1007/978-3-319-12188-8_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12187-1

  • Online ISBN: 978-3-319-12188-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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