Budgeted Online Influence Maximization

Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020)

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Pierre Perrault, Jennifer Healey, Zheng Wen, Michal Valko


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 models better the real-world setting where the cost of influencers varies and advertizers 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.