The influence maximization (IM) problem involves identifying a set of key individuals in a social network who can maximize the spread of influence through their network connections. With the advent of geometric deep learning on graphs, great progress has been made towards better solutions for the IM problem. In this paper, we focus on the dynamic non-progressive IM problem, which considers the dynamic nature of real-world social networks and the special case where the influence diffusion is non-progressive, i.e., nodes can be activated multiple times. We first extend an existing diffusion model to capture the non-progressive influence propagation in dynamic social networks. We then propose the method, DNIMRL, which employs deep reinforcement learning and dynamic graph embedding to solve the dynamic non-progressive IM problem. In particular, we propose a novel algorithm that effectively leverages graph embedding to capture the temporal changes of dynamic networks and seamlessly integrates with deep reinforcement learning. The experiments, on different types of real-world social network datasets, demonstrate that our method outperforms state-of-the-art baselines.

翻译：影响力最大化（IM）问题旨在识别社交网络中的一组关键个体，使其能够通过网络连接最大化影响力的传播。随着图几何深度学习的兴起，针对IM问题的解决方案已取得重大进展。本文聚焦于动态非渐进式IM问题，该问题考虑了现实世界社交网络的动态特性以及影响力扩散为非渐进式的特殊情况，即节点可被多次激活。我们首先扩展了现有扩散模型，以捕捉动态社交网络中的非渐进式影响力传播。随后，我们提出了DNIMRL方法，该方法采用深度强化学习与动态图嵌入技术来解决动态非渐进式IM问题。特别地，我们提出了一种新颖算法，能有效利用图嵌入捕捉动态网络的时序变化，并与深度强化学习无缝集成。在不同类型的真实世界社交网络数据集上的实验表明，我们的方法优于当前最先进的基线模型。