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.
Modelwire context
ExplainerThe real shift here is epistemological: most prior influence maximization work treats 'how many influencers' as the binding constraint, but real ad campaigns are constrained by dollars, not headcount. Modeling cost heterogeneity directly changes which theoretical guarantees are achievable and how regret accumulates over time.
The closest prior coverage on Modelwire is the log-barrier bandit feedback paper from arXiv on April 16, which also works in the bandit feedback regime and proves convergence bounds in adversarial settings. That paper addressed zero-sum matrix games; this one addresses a cooperative diffusion problem, so the overlap is methodological rather than applied. The broader archive here skews heavily toward deployed AI products and funding rounds, and this paper sits largely disconnected from that activity. It belongs instead to a quieter thread of algorithmic marketing science research that rarely surfaces in product announcements but quietly informs how platforms like Meituan (covered April 16) design their merchant promotion tooling.
Watch whether any major social ad platform (Meta, TikTok, or a DSP vendor) cites this budget-constrained formulation in a product paper or engineering blog within the next 12 months. Adoption at that layer would confirm the theoretical bounds are tight enough to be operationally useful.
This analysis is generated by Modelwire’s editorial layer from our archive and the summary above. It is not a substitute for the original reporting. How we write it.
MentionsIndependent cascade model · Semi-bandit feedback · Influence maximization
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