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URN: urn:nbn:de:0030-drops-115208
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On Adaptivity Gaps of Influence Maximization Under the Independent Cascade Model with Full-Adoption Feedback

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Abstract

In this paper, we study the adaptivity gap of the influence maximization problem under the independent cascade model when full-adoption feedback is available. Our main results are to derive upper bounds on several families of well-studied influence graphs, including in-arborescences, out-arborescences and bipartite graphs. Especially, we prove that the adaptivity gap for the in-arborescences is between [e/(e-1), 2e/(e-1)], and for the out-arborescences the gap is between [e/(e-1), 2]. These are the first constant upper bounds in the full-adoption feedback model. Our analysis provides several novel ideas to tackle the correlated feedback appearing in adaptive stochastic optimization, which may be of independent interest.

BibTeX - Entry

@InProceedings{chen_et_al:LIPIcs:2019:11520,
  author =	{Wei Chen and Binghui Peng},
  title =	{{On Adaptivity Gaps of Influence Maximization Under the Independent Cascade Model with Full-Adoption Feedback}},
  booktitle =	{30th International Symposium on Algorithms and Computation (ISAAC 2019)},
  pages =	{24:1--24:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-130-6},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{149},
  editor =	{Pinyan Lu and Guochuan Zhang},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2019/11520},
  URN =		{urn:nbn:de:0030-drops-115208},
  doi =		{10.4230/LIPIcs.ISAAC.2019.24},
  annote =	{Keywords: Adaptive influence maximization, adaptivity gap, full-adoption feedback}
}

Keywords: Adaptive influence maximization, adaptivity gap, full-adoption feedback
Seminar: 30th International Symposium on Algorithms and Computation (ISAAC 2019)
Issue date: 2019
Date of publication: 28.11.2019


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