提出新方法解决动态社交网络中可重复激活的影响力传播问题
Non-Progressive Influence Maximization in Dynamic Social Networks
- 用动态图嵌入结合强化学习，捕捉网络随时间变化的特性
- 在真实社交网络数据集上显著优于现有最佳方法
- 适合研究动态网络影响力传播的学者与从业者
影响最大化（IM）问题旨在识别社交网络中能最大范围传播影响力的个体。随着图上几何深度学习的发展，该问题已取得显著进展。本文聚焦于动态非渐进式影响最大化问题，考虑真实社交网络的动态性以及影响传播非渐进的特殊情形——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传播机制；随后提出DNIMRL方法，结合深度强化学习与动态图嵌入来求解该问题。特别地，我们设计了一种新颖算法，有效利用图嵌入捕获网络的时序变化，并与深度强化学习无缝集成。在多种真实社交网络数据集上的实验表明，该方法优于当前最先进的基线模型。
原文摘要 · Abstract (English)
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.
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