MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization
CoRR(2024)
摘要
Multiplex influence maximization (MIM) asks us to identify a set of seed
users such as to maximize the expected number of influenced users in a
multiplex network. MIM has been one of central research topics, especially in
nowadays social networking landscape where users participate in multiple online
social networks (OSNs) and their influences can propagate among several OSNs
simultaneously. Although there exist a couple combinatorial algorithms to MIM,
learning-based solutions have been desired due to its generalization ability to
heterogeneous networks and their diversified propagation characteristics. In
this paper, we introduce MIM-Reasoner, coupling reinforcement learning with
probabilistic graphical model, which effectively captures the complex
propagation process within and between layers of a given multiplex network,
thereby tackling the most challenging problem in MIM. We establish a
theoretical guarantee for MIM-Reasoner as well as conduct extensive analyses on
both synthetic and real-world datasets to validate our MIM-Reasoner's
performance.
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