计算机应用研究2025，Vol.42Issue(9)：2637-2643,7.DOI:10.19734/j.issn.1001-3695.2025.02.0027
基于传播特征强化学习的社交网络信息传播关键用户发现方法
Key user identification method in social network information propagation based on propagation features reinforcement learning
摘要
Abstract
The traditional problem of influence maximizing aims to select a certain number of source seeds to publish specific information,so as to maximize the influence spread of the information.However,seed users selected by algorithms may not ne-cessarily be willing to publish the specified information.In addition,traditional influence maximization algorithms need to be re-run on networks with different structures,resulting in lower efficiency.To address these issues,this paper firstly formalized the problem of maximizing influence as a new key user identification for information propagation(KUIP)problem,which involved how to discover a certain number of key users without requiring them to publish specific information,but by intervening in their attitudes and tendencies towards disseminating information,to maximize the influence spread of that information.In order to more accurately describe the information propagation scenario,this paper proposed an adjustable threshold model(ATM)to simulate the attitude tendencies and environmental influences of users in disseminating information.Furthermore,in order to en-sure the efficiency and effectiveness of key user identification on networks with different structures,this paper proposed a key user identification based on propagation reinforcement learning(KPRL),which utilized graph attention mechanism to learn the propagation features of users and trained the model parameters using double deep Q-network(DDQN).Experiments on six real network datasets show that KPRL improves the influence spread indicator by an average of 11.7％,surpassing existing baseline methods and demonstrating its effectiveness in the field of key user identification.关键词
影响力最大化/关键用户发现/深度强化学习/图注意力机制Key words
influence maximization/key user identification/deep reinforcement learning/graph attention mechanism分类
信息技术与安全科学引用本文复制引用
刘晓亮,张鹏飞..基于传播特征强化学习的社交网络信息传播关键用户发现方法[J].计算机应用研究,2025,42(9):2637-2643,7.基金项目
装备基金项目 （）
军队重点院校科研专项 （）
战区级重点实验室自主课题 （）
