标题： 备注：一种可扩展的强化多专家框架用于多层影响力最大化 显示英文标题

标题： REM: A Scalable Reinforced Multi-Expert Framework for Multiplex Influence Maximization

摘要： 在社交在线平台上，识别有影响力的种子用户以最大化影响力传播是至关重要的，因为它可以大大降低信息传播所需的成本和努力。 虽然有效，传统的多重影响最大化（MIM）方法已达到其性能极限，促使基于学习的方法的出现。 这些新方法旨在实现更好的泛化能力和可扩展性，以处理更大规模的图，但面临重大挑战，例如（1）无法处理未知的扩散模式和（2）依赖高质量的训练样本。 为了解决这些问题，我们提出了强化专家最大化框架（REM）。 REM利用传播专家混合技术，以有效地编码大型多重网络的动态传播，从而生成增强的影响传播。 值得注意的是，REM将生成模型视为一种策略，自主生成不同的种子集，并从强化学习的角度学习如何改进它们。 在多个真实数据集上的大量实验表明，REM在影响传播、可扩展性和影响最大化任务中的推理时间方面优于最先进的方法。 显示英文摘要

摘要： In social online platforms, identifying influential seed users to maximize influence spread is a crucial as it can greatly diminish the cost and efforts required for information dissemination. While effective, traditional methods for Multiplex Influence Maximization (MIM) have reached their performance limits, prompting the emergence of learning-based approaches. These novel methods aim for better generalization and scalability for more sizable graphs but face significant challenges, such as (1) inability to handle unknown diffusion patterns and (2) reliance on high-quality training samples. To address these issues, we propose the Reinforced Expert Maximization framework (REM). REM leverages a Propagation Mixture of Experts technique to encode dynamic propagation of large multiplex networks effectively in order to generate enhanced influence propagation. Noticeably, REM treats a generative model as a policy to autonomously generate different seed sets and learn how to improve them from a Reinforcement Learning perspective. Extensive experiments on several real-world datasets demonstrate that REM surpasses state-of-the-art methods in terms of influence spread, scalability, and inference time in influence maximization tasks.