摘要
基于网络拓扑结构的社交网络影响力最大化算法受网络结构影响大,导致在不同规模、不同拓扑结构的社交网络上的性能不稳定。针对此问题,提出一种基于改进Transformer模型的社交网络影响力最大化算法。首先,基于K-shell分解法筛选社交网络中影响力高的节点;然后,运用随机游走策略发现候选节点的拓扑结构信息和连接框架信息;最终,对Transformer模型进行改进,使其支持可扩展的节点特征序列,利用改进Transformer模型预测社交网络中的种子节点。在6个不同规模的真实社交网络上完成了验证实验。结果表明,所提算法在不同规模、不同拓扑结构的社交网络上均实现了较好的影响力最大化性能,且大幅提高了种子节点识别的时间效率。
The network topology structure based influence maximization algorithms are greatly influenced by the network structure,which leads to unstable performance of social networks of different scales and different topology structures.In view of this problem,a improved Transformer model based social network influence maximization algorithm was proposed.Firstly,the high influential nodes of the society network were selected based on the k-shell decomposition method.Seconcly,the topology structure information and connection framework information of the candidate nodes were discovered by use of the random walk strategy.Finally,the Transformer model was improved,in order to support scalable node feature sequences,and the improved Transformer model was taken advantage to predict the seed nodes of the social network.Validation experiments were carried on six real social networks of different scales.The results show that the proposed algorithm realizes a good influence maximization performance on social networks of different scales and topology structures,and the time efficiency of the seed node recognition has been increased significantly.
作者
于树科
姚瑶
严晨雪
YU Shuke;YAO Yao;YAN Chenxue(School of Electronics and Information,Jiangsu Vocational College of Business,Nantong 226011,China)
出处
《电信科学》
北大核心
2024年第12期114-124,共11页
Telecommunications Science
关键词
社交网络
影响力节点
影响力最大化
信息传播
神经网络
social network
influence node
influence maximization
information propagation
neural network
