Fractional Budget Allocation for Influence Maximization

Abhishek K. Umrawal, Vaneet Aggarwal, Christopher J. Quinn

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We consider a generalization of the widely studied discrete influence maximization problem. We consider that instead of marketers using a budget to send free products to a few influencers, they can provide discounts to partly incentivize a larger set of influencers with the same budget. We show that this problem is an instance of maximizing the multilinear extension of a monotone submodular set function subject to an L1 constraint. We propose and analyze an efficient (1-1/e) - approximation algorithm. We run experiments on a real-world social network to show the performance of our method in contrast to methods proposed for other generalizations of influence maximization.

Original languageEnglish (US)
Title of host publication2023 62nd IEEE Conference on Decision and Control, CDC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9798350301243
StatePublished - 2023
Event62nd IEEE Conference on Decision and Control, CDC 2023 - Singapore, Singapore
Duration: Dec 13 2023Dec 15 2023

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Conference62nd IEEE Conference on Decision and Control, CDC 2023

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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