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.

翻译：多层影响力最大化问题要求我们识别一组种子用户，以最大化多层网络中受影响的期望用户数。该问题一直是核心研究课题之一，尤其在当今用户同时参与多个在线社交网络、其影响力可跨平台传播的社交网络格局下。尽管针对MIM问题已有若干组合优化算法，但由于学习型方法对异质网络及其多样化传播特性具有泛化能力，基于学习的方法一直备受期待。本文提出MIM-Reasoner框架，将强化学习与概率图模型相结合，有效捕捉给定多层网络内层间与层内的复杂传播过程，从而攻克MIM中最具挑战性的难题。我们为MIM-Reasoner建立了理论保证，并在合成数据集与真实数据集上开展了广泛分析以验证其性能。