Budgeted Online Influence Maximization

2026-04-21

AI Summary

This work addresses the limitations of existing online influence maximization methods, which typically overlook the heterogeneous costs of influencers and rely solely on fixed cardinality constraints. To better reflect real-world social advertising scenarios under limited advertiser budgets, this paper proposes the first online influence maximization framework subject to a total budget constraint. The framework integrates the independent cascade diffusion model with an edge-level semi-bandit feedback mechanism, enabling dynamic selection of cost-effective influencer sets through online learning to maximize influence spread. Theoretical analysis yields a regret bound that improves upon existing approaches, and extensive experiments demonstrate the algorithm’s effectiveness and superiority under both budget-constrained and traditional cardinality-constrained settings.

Technology Category

• Machine Learning: Online Learning & Bandits
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising

Abstract

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.

Problem

• Budgeted Online Influence Maximization
• Influencer Cost
• Social Advertising Budget
• Independent Cascade Model
• Semi-bandit Feedback

Innovation

• Budgeted Influence Maximization
• Online Learning
• Semi-bandit Feedback
• Independent Cascade Model
• Regret Bound