Abstract
In this work we introduce a new type of Independent Cascade Model (ICM) that goes beyond current ICMs by incorporating the effects of information salience and exposure that we call ICM-SE. ICMs are a well-studied class of models designed to capture information spreading on networks where the standard objective is to identify a set of nodes to seed with information such that some measure of information spread is maximized. To our knowledge, we are the first to incorporate the effects of salience and exposure within the ICM framework, which brings additional realism to our ICM, but also introduces additional challenges in identifying good strategies for information seeding. Therefore, our second contribution is to introduce and demonstrate the effectiveness of a powerful class of search algorithms called Estimation of Distribution Algorithms (EDA) for this type of problem. We show that our EDA approach outperforms the typical greedy search approach on graphs drawn from various popular network classes. We also identify and investigate quantitative and qualitative differences between the strategies from EDA versus those from greedy search.
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Martin, C.E., Warmsley, D., Johnson, S.D. (2020). Optimizing Attention-Aware Opinion Seeding Strategies. In: Thomson, R., Bisgin, H., Dancy, C., Hyder, A., Hussain, M. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2020. Lecture Notes in Computer Science(), vol 12268. Springer, Cham. https://doi.org/10.1007/978-3-030-61255-9_10
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DOI: https://doi.org/10.1007/978-3-030-61255-9_10
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