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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\u2014the 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.<\/jats:p>","DOI":"10.1007\/s10618-025-01137-z","type":"journal-article","created":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T13:07:30Z","timestamp":1753708050000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Inductive influence estimation and maximization over unseen social networks under two diffusion models"],"prefix":"10.1007","volume":"39","author":[{"given":"Jihoon","family":"Ko","sequence":"first","affiliation":[]},{"given":"Sojeong","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Kyuhan","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Shinhwan","family":"Kang","sequence":"additional","affiliation":[]},{"given":"Dongyeong","family":"Hwang","sequence":"additional","affiliation":[]},{"given":"Kijung","family":"Shin","sequence":"additional","affiliation":[]},{"given":"Noseong","family":"Park","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,28]]},"reference":[{"key":"1137_CR1","doi-asserted-by":"publisher","unstructured":"Bello I, Pham H, Le QV, et\u00a0al. 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