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

TL;DR

This paper introduces REM, a scalable reinforcement learning framework that effectively identifies influential users in multiplex social networks, overcoming limitations of traditional methods and improving influence spread and efficiency.

Contribution

REM employs a Propagation Mixture of Experts and reinforcement learning to handle unknown diffusion patterns and scale to large multiplex networks, advancing influence maximization techniques.

Findings

REM outperforms state-of-the-art methods in influence spread.

REM demonstrates superior scalability and inference speed.

Experimental results validate REM's effectiveness on real-world datasets.

Abstract

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.