  Influence maximization (IM) is one of the most important problems in social
network analysis. Its objective is to find a given number of seed nodes that
maximize the spread of information through a social network. Since it is an
NP-hard problem, many approximate/heuristic methods have been developed, and a
number of them repeat Monte Carlo (MC) simulations over and over to reliably
estimate the influence (i.e., the number of infected nodes) of a seed set. In
this work, we present an inductive machine learning method, called Monte Carlo
Simulator (MONSTOR), for estimating the influence of given seed nodes in social
networks unseen during training. To the best of our knowledge, MONSTOR is the
first inductive method for this purpose. MONSTOR can greatly accelerate
existing IM algorithms by replacing repeated MC simulations. In our
experiments, MONSTOR provided highly accurate estimates, achieving 0.998 or
higher Pearson and Spearman correlation coefficients in unseen real-world
social networks. Moreover, IM algorithms equipped with MONSTOR are more
accurate than state-of-the-art competitors in 63% of IM use cases.
