Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback
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We study the stochastic online problem of learning to influence in a social network with semi-bandit feedback, where we observe how users influence each other. The problem combines challenges of limited feedback, because the learning agent only observes the influenced portion of the network, and combinatorial number of actions, because the cardinality of the feasible set is exponential in the maximum number of influencers. We propose a computationally efficient UCB-like algorithm, IMLinUCB, and analyze it. Our regret bounds are polynomial in all quantities of interest; reflect the structure of the network and the probabilities of influence. Moreover, they do not depend on inherently large quantities, such as the cardinality of the action set. To the best of our knowledge, these are the first such results. IMLinUCB permits linear generalization and therefore is suitable for large-scale problems. Our experiments show that the regret of IMLinUCB scales as suggested by our upper bounds in several representative graph topologies; and based on linear generalization, IMLinUCB can significantly reduce regret of real-world influence maximization semi-bandits.
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