  Since its introduction in 2003, the influence maximization (IM) problem has
drawn significant research attention in the literature. The aim of IM is to
select a set of k users who can influence the most individuals in the social
network. The problem is proven to be NP-hard. A large number of approximate
algorithms have been proposed to address this problem. The state-of-the-art
algorithms estimate the expected influence of nodes based on sampled diffusion
paths. As the number of required samples have been recently proven to be lower
bounded by a particular threshold that presets tradeoff between the accuracy
and efficiency, the result quality of these traditional solutions is hard to be
further improved without sacrificing efficiency. In this paper, we present an
orthogonal and novel paradigm to address the IM problem by leveraging deep
learning models to estimate the expected influence. Specifically, we present a
novel framework called DISCO that incorporates network embedding and deep
reinforcement learning techniques to address this problem. Experimental study
on real-world networks demonstrates that DISCO achieves the best performance
w.r.t efficiency and influence spread quality compared to state-of-the-art
classical solutions. Besides, we also show that the learning model exhibits
good generality.
