  We study the problem of online influence maximization in social networks. In
this problem, a learner aims to identify the set of "best influencers" in a
network by interacting with it, i.e., repeatedly selecting seed nodes and
observing activation feedback in the network. We capitalize on an important
property of the influence maximization problem named network assortativity,
which is ignored by most existing works in online influence maximization. To
realize network assortativity, we factorize the activation probability on the
edges into latent factors on the corresponding nodes, including influence
factor on the giving nodes and susceptibility factor on the receiving nodes. We
propose an upper confidence 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 influence
maximization algorithm. And extensive empirical evaluations on two real-world
networks showed the effectiveness of our proposed solution.
