  We study the online influence maximization (OIM) problem in social networks,
where the learner repeatedly chooses seed nodes to generate cascades, observes
the cascade feedback, and gradually learns the best seeds that generate the
largest cascade in multiple rounds. In the demand of the real world, we work
with node-level feedback instead of the common edge-level feedback in the
literature. The edge-level feedback reveals all edges that pass through
information in a cascade, whereas the node-level feedback only reveals the
activated nodes with timestamps. The node-level feedback is arguably more
realistic since in practice it is relatively easy to observe who is influenced
but very difficult to observe from which relationship (edge) the influence
comes. Previously, there is a nearly optimal $\tilde{O}(\sqrt{T})$-regret
algorithm for OIM problem under the linear threshold (LT) diffusion model with
node-level feedback. It remains unknown whether the same algorithm exists for
the independent cascade (IC) diffusion model. In this paper, we resolve this
open problem by presenting an $\tilde{O}(\sqrt{T})$-regret algorithm for OIM
problem under the IC model with node-level feedback.
