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Budgeted Online Influence Maximization

arXiv 2604.19672 ↗First posted 21 Apr 2026cs.LG stat.MLcited by 0

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Abstract (arXiv)

We introduce a new budgeted framework for online influence maximization, considering the total cost of an advertising campaign instead of the common cardinality constraint on a chosen influencer set. Our approach better models the real-world setting where the cost of influencers varies and advertisers want to find the best value for their overall social advertising budget. We propose an algorithm assuming an independent cascade diffusion model and edge level semi-bandit feedback, and provide both theoretical and experimental results. Our analysis is also valid for the cardinality constraint setting and improves the state of the art regret bound in this case.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 47 references that are in the map. First posted on arXiv 21 Apr 2026.

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13 of 47 references.

References · 13

  1. 2014Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Armscited by 78
  2. 2018Thompson Sampling for Combinatorial Semi-Banditscited by 78
  3. 2014Lipschitz Bandits: Regret Lower Bounds and Optimal Algorithmscited by 59
  4. 2017Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applicationscited by 44
  5. 2014Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiencycited by 33
  6. 2020Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Banditscited by 30
  7. 2016Combinatorial semi-bandit with known covariancecited by 24
  8. 2015Influence Maximization with Banditscited by 12
  9. 2014Sketch-based Influence Maximization and Computation: Scaling up with Guaranteescited by 9
  10. 2018Online Influence Maximization with Local Observationscited by 3
  11. 2018Finding the bandit in a graph: Sequential search-and-stopcited by 3
  12. 2020Online Learning with Cumulative Oversampling: Application to Budgeted Influence Maximizationcited by 2
1 more
  1. 2026Covariance-adapting algorithm for semi-bandits with application to sparse rewardscited by 1

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  1. 2022Contextual Bandits for Advertising Campaigns: A Diffusion-Model Independent Approach (Extended Version)
  2. 2021Online Influence Maximization under the Independent Cascade Model with Node-Level Feedback
  3. 2025A Community-Aware Framework for Influence Maximization with Explicit Accounting for Inter-Community Influence
  4. 2024Graph Bayesian Optimization for Multiplex Influence Maximization
  5. 2025A Pressure-Based Diffusion Model for Influence Maximization on Social Networks
  6. 2025REM: A Scalable Reinforced Multi-Expert Framework for Multiplex Influence Maximization
  7. 2023DSCom: A Data-Driven Self-Adaptive Community-Based Framework for Influence Maximization in Social Networks
  8. 2020Influence Maximization Under Generic Threshold-based Non-submodular Model ↗