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An L p Norm Relaxation Approach to Positive Influence Maximization in Social Network under the Deterministic Linear Threshold Model

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Algorithms and Models for the Web Graph (WAW 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8305))

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Abstract

In this paper, an Influence Maximization problem in Social Network under the Deterministic Linear Threshold model is considered. The objective is to minimize the number of eventually negatively opinionated nodes in the network in a dynamic setting. The main ingredient of the new approach is the application of the sparse optimization technique. In the presence of inequality constraints and nonlinear relationships, the standard convex relaxation method of the L 1 relaxation does not perform well in this context. Therefore we propose to apply the L p relaxation where 0 < p < 1. The resulting optimization model is therefore non-convex. By means of an interior point method, the model can be solved efficiently and stably, typically yielding robust and sparse solutions in our numerical experiments with the simulated data.

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Author information

Authors and Affiliations

  1. Department of Computer Science, The University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX, 75080, USA

    Rupei Xu

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  1. Rupei Xu
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Editor information

Editors and Affiliations

  1. Department of Mathematics, Ryerson University, Toronto, ON, Canada

    Anthony Bonato  & Paweł Prałat  & 

  2. School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    Michael Mitzenmacher

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Xu, R. (2013). An L p Norm Relaxation Approach to Positive Influence Maximization in Social Network under the Deterministic Linear Threshold Model. In: Bonato, A., Mitzenmacher, M., Prałat, P. (eds) Algorithms and Models for the Web Graph. WAW 2013. Lecture Notes in Computer Science, vol 8305. Springer, Cham. https://doi.org/10.1007/978-3-319-03536-9_12

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  • DOI: https://doi.org/10.1007/978-3-319-03536-9_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03535-2

  • Online ISBN: 978-3-319-03536-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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