MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks
This addresses the computational bottleneck of repeated Monte Carlo simulations in social network analysis, offering a faster and more accurate solution for influence maximization tasks.
The paper tackles the influence maximization problem in social networks by introducing MONSTOR, an inductive machine learning method that estimates influence for unseen networks, achieving correlation coefficients of 0.998 or higher and improving accuracy in 63% of use cases compared to state-of-the-art competitors.
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.