Budgeted Online Influence Maximization

Researchers propose a budget-constrained algorithm for selecting influencers in social ad campaigns, replacing traditional cardinality limits with real-world cost modeling. The approach improves regret bounds for both budget and cardinality settings under cascade diffusion models with semi-bandit feedback.

MentionsIndependent cascade model · Semi-bandit feedback · Influence maximization

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