Influence maximization in social media networks concerning dynamic user behaviors via reinforcement learning

Influence maximization in social media networks concerning dynamic user behaviors via reinforcement learning

Abstract This study examines the influence maximization (IM) problem via information cascades within random graphs, the topology of which dynamically changes due to the uncertainty of user behavior. This study leverages the discrete choice model (DCM) to calculate the probabilities of the existence...

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Bibliographic Details
Main Authors: Mengnan Chen, Qipeng P. Zheng, Vladimir Boginski, Eduardo L. Pasiliao
Format: Article
Language:English
Published: SpringerOpen 2021-02-01
Series:Computational Social Networks
Subjects:
Social networks
Markov decision process
Discrete choice model
Influence maximization
Multistage stochastic programming
Reinforcement learning
Online Access:https://doi.org/10.1186/s40649-021-00090-3

Internet

https://doi.org/10.1186/s40649-021-00090-3

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