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Influence maximization (IM) is one of the most important problems in socialnetwork analysis. Its objective is to find a given number of seed nodes thatmaximize the spread of information through a social network. Since it is anNP-hard problem, many approximate/heuristic methods have been developed, and anumber of them repeat Monte Carlo (MC) simulations over and over to reliablyestimate the influence (i.e., the number of infected nodes) of a seed set. Inthis work, we present an inductive machine learning method, called Monte CarloSimulator (MONSTOR), for estimating the influence of given seed nodes in socialnetworks unseen during training. To the best of our knowledge, MONSTOR is thefirst inductive method for this purpose. MONSTOR can greatly accelerateexisting IM algorithms by replacing repeated MC simulations. In ourexperiments, MONSTOR provided highly accurate estimates, achieving 0.998 orhigher Pearson and Spearman correlation coefficients in unseen real-worldsocial networks. Moreover, IM algorithms equipped with MONSTOR are moreaccurate than state-of-the-art competitors in 63% of IM use cases.

MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks