Abstract
This paper studies the Influence Maximization problem based on information cascading within a random graph, where the network structure is dynamically changing according to users’ uncertain behaviors. The Discrete Choice Model is used to define the probability distribution of the directed arcs between any two nodes in the random graph. The discrete choice model provides a good description and prediction of user behavior following/unfollowing their neighbor node. To find the maximal influence at the end of the finite time horizon, this paper proposed Multi-Stage Stochastic Programming models, which can help the decision maker to select the optimal seed nodes to broadcast messages efficiently. To approximate the optimal decisions, the paper discuss two approaches, i.e., the Myopic Two-Stage Stochastic Programming at each time period, and Reinforcement Learning for Markov Decision Process. Computational experiments show that the Reinforcement Learning method exhibits better performance than the Myopic method in large-scale networks.
This material is based on work supported by the AFRL Mathematical Modeling and Optimization Institute.
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Chen, M., Zheng, Q.P., Boginski, V., Pasiliao, E.L. (2019). Reinforcement Learning in Information Cascades Based on Dynamic User Behavior. In: Tagarelli, A., Tong, H. (eds) Computational Data and Social Networks. CSoNet 2019. Lecture Notes in Computer Science(), vol 11917. Springer, Cham. https://doi.org/10.1007/978-3-030-34980-6_17
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DOI: https://doi.org/10.1007/978-3-030-34980-6_17
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