Inductive influence estimation and maximization over unseen social networks under two diffusion models

Jihoon Ko, Sojeong Kim, Kyuhan Lee, Shinhwan Kang, Dongyeong Hwang, Kijung Shin, Noseong Park

Abstract: Influence estimation (IE) and influence maximization (IM) are among the most extensively studied problems in social network analysis. Assuming diffusion (i.e., the spread of diseases) within a social network, IE aims to estimate the influence (i.e., the number of infected nodes) for a given set of seeds; and IM aims to identify a given number of seed nodes that maximize the influence. For both IE and IM, widely-adopted strategies involve repeating Monte Carlo (MC) simulations of diffusion over and over for various seed sets, which is computationally expensive. In this work, we present Monte Carlo Simulator+ (MONSTOR+), an inductive machine learning method designed to estimate the influence of given seed-node sets in social networks under two diffusion models—the independent cascade (IC) model and the linear threshold (LT) model. Due to its inductive nature, MONSTOR+ is applicable to seed-node sets and social networks not included in the training data. MONSTOR+, with its ability to accurately estimate influence through a single forward pass, can greatly accelerate existing IM algorithms by replacing repeated MC simulations. In our experiments, MONSTOR+ exhibits high IE accuracy, achieving 0.955 or higher Pearson and Spearman correlation coefficients in unseen real-world social networks. Notably, MONSTOR+ is about 5 to 3000 times faster than repeated MC simulations with similar IE accuracy. For IM problems, IM algorithms equipped with MONSTOR+ are more accurate than state-of-the-art competitors in 81.5 and 77.8% of IM use cases under the IC model and LT model, respectively.

External IDs:dblp:journals/datamine/KoKLKHSP25