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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月28日

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

摘要

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.