On Adaptivity Gaps of Influence Maximization Under the Independent Cascade Model with Full-Adoption Feedback

Authors Wei Chen, Binghui Peng

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Wei Chen
  • Microsoft Research, Beijing, China
Binghui Peng
  • Columbia University, New York, United States

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Wei Chen and Binghui Peng. On Adaptivity Gaps of Influence Maximization Under the Independent Cascade Model with Full-Adoption Feedback. In 30th International Symposium on Algorithms and Computation (ISAAC 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 149, pp. 24:1-24:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


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.

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ACM Subject Classification
  • Theory of computation → Social networks
  • Adaptive influence maximization
  • adaptivity gap
  • full-adoption feedback


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