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.

翻译：我们提出了一种新的预算约束框架用于在线影响力最大化问题，该框架考虑广告活动的总成本，而非对所选影响者集合的常见基数约束。我们的方法能更好地模拟真实场景（如影响者成本各异，广告主希望找到最佳整体社交广告预算价值）。我们提出了一种基于独立级联扩散模型和边级半强盗反馈的算法，并提供了理论与实验验证结果。该分析在基数约束场景下同样适用，且在此情况下改进了当前最优的遗憾界。