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insight - Multiplex Influence Maximization
MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization
MIM-Reasoner introduces a novel framework combining reinforcement learning and probabilistic graphical models to maximize influence in multiplex networks.
Optimizing Multiplex Influence Maximization in Social Networks through Graph Bayesian Optimization
The core message of this paper is to introduce the Multiplex Influence Maximization (Multi-IM) problem, which aims to maximize the influence spread of multiple interconnected information items in a social network, and to propose a Graph Bayesian Optimization framework (GBIM) to effectively solve this problem.
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