
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.


coremsg

optimizing-multiplex-influence-maximization-in-social-networks-through-graph-bayesian-optimization


Optimizing Multiplex Influence Maximization in Social Networks through Graph Bayesian Optimization


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MIM-Reasoner introduces a novel framework combining reinforcement learning and probabilistic graphical models to maximize influence in multiplex networks.


mim-reasoner-learning-with-theoretical-guarantees-for-multiplex-influence-maximization


MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization
