Budgeted Online Influence Maximization
This work addresses the practical need for cost-aware influencer selection in social advertising, offering a more realistic model and improved theoretical guarantees.
The paper introduces a budgeted framework for online influence maximization that accounts for varying influencer costs, and proposes an algorithm with improved regret bounds, achieving state-of-the-art performance in both budgeted and cardinality-constrained settings.
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.