In this paper, we study the problem of online in uence maximization in social networks. In this problem, a learner aims to identify the set of "best in uencers" in a network by interacting with the network, i.e., repeatedly selecting seed nodes and observing activation feedback in the network. We capitalize on an important property of the in uence maximization problem named network assortativity, which is ignored by most existing works in online in uence maximization. To realize network assortativity, we factorize the activation probability on the edges into latent factors on the corresponding nodes, including in uence factor on the giving nodes and susceptibility factor on the receiving nodes. We propose an upper con dence bound based online learning solution to estimate the latent factors, and therefore the activation probabilities. Considerable regret reduction is achieved by our factorization based online in uence maximization algorithm. Extensive empirical evaluations on two real-world networks showed the e ectiveness of our proposed solution.
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