REM：一种用于多重影响最大化的可扩展强化多专家框架
REM: A Scalable Reinforced Multi-Expert Framework for Multiplex Influence Maximization
AI总结：
本文提出REM框架，结合传播专家混合与强化学习，自主生成并优化种子集，以解决多重影响最大化中未知传播模式和训练样本依赖问题，并在多个真实数据集上验证其在影响传播、可扩展性和推理时间上优于现有方法。
摘要：
在社交在线平台中，识别有影响力的种子用户以最大化影响传播至关重要，因为这可以大幅降低信息传播所需的成本和工作量。虽然传统方法在多重影响最大化（Multiplex Influence Maximization，MIM）中有效，但已达到性能极限，促使基于学习的方法出现。这些新方法旨在对更大规模的图实现更好的泛化性和可扩展性，但面临重大挑战，例如（1）无法处理未知的传播模式，以及（2）依赖高质量的训练样本。为解决这些问题，我们提出了强化专家最大化框架（Reinforced Expert Maximization framework，REM）。REM利用传播专家混合（Propagation Mixture of Experts）技术，有效编码大型多重网络的动态传播，以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，以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。在多个真实世界数据集上的大量实验表明，REM在影响最大化任务中的影响传播、可扩展性和推理时间方面超越了最先进的方法。
英文摘要：
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.
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