Influence Maximization (IM) stands as a central challenge within the domain of complex network analysis, with the primary objective of identifying an optimal seed set of a predetermined size that maximizes the reach of influence propagation. Over time, numerous methodologies have been proposed to address the IM problem. However, one certain network referred to as Imbalanced Heterogeneous Networks (IHN), which widely used in social situation, urban and rural areas, and merchandising, presents challenges in achieving high-quality solutions. In this work, we introduce the Lightweight Reinforcement Learning algorithm with Prior knowledge (LRLP), which leverages the Struc2Vec graph embedding technique that captures the structural similarity of nodes to generate vector representations for nodes within the network. In details, LRLP incorporates prior knowledge based on a group of centralities, into the initial experience pool, which accelerates the reinforcement learning training for better solutions. Additionally, the node embedding vectors are input into a Deep Q Network (DQN) to commence the lightweight training process. Experimental evaluations conducted on synthetic and real networks showcase the effectiveness of the LRLP algorithm. Notably, the improvement seems to be more pronounced when the the scale of the network is larger. We also analyze the effect of different graph embedding algorithms and prior knowledge on algorithmic results. Moreover, we conduct an analysis about some parameters, such as number of seed set selections T, embedding dimension d and network update frequency C. It is significant that the reduction of number of seed set selections T not only keeps the quality of solutions, but lowers the algorithm’s computational cost.

影响力最大化 （IM） 是复杂网络分析领域中的核心挑战，其主要目标是确定预定大小的最佳种子集，以最大限度地扩大影响力传播的范围。随着时间的推移，已经提出了许多方法来解决 IM 问题。然而，一个被称为不平衡异构网络 （IHN） 的某个网络广泛用于社会情境、城市和农村地区以及商品销售，对实现高质量的解决方案提出了挑战。在这项工作中，我们介绍了先验知识的轻量级强化学习算法 （LRLP），该算法利用 Struc2Vec 图嵌入技术来捕获节点的结构相似性，为网络内的节点生成向量表示。具体来说，LRLP 将基于一组中心的先验知识整合到初始经验库中，从而加速强化学习训练以获得更好的解决方案。此外，节点嵌入向量被输入到深度 Q 网络 （DQN） 中，以开始轻量级训练过程。在合成网络和真实网络上进行的实验评估证明了 LRLP 算法的有效性。值得注意的是，当网络规模更大时，改进似乎更加明显。我们还分析了不同图嵌入算法和先验知识对算法结果的影响。此外，我们对一些参数进行了分析，例如种子集选择的数量 T 、嵌入维度 d 和网络更新频率 C。 重要的是，减少种子集选择 T 的数量不仅可以保持解的质量，而且降低了算法的计算成本。