北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2025, Vol. 48 ›› Issue (1): 52-58.

• 论文 • 上一篇    下一篇

基于节点嵌入的机会网络节点重要度评估

刘琳岚1, 崔 辉1, 高浩轩2, 舒 坚2, 江宇楠1   

  1. 1. 南昌航空大学 信息工程学院 2. 南昌航空大学 软件学院
  • 收稿日期:2023-11-01 修回日期:2024-01-18 出版日期:2025-02-26 发布日期:2025-02-25
  • 通讯作者: 刘琳岚 E-mail:liulinlan@nchu.edu.cn
  • 基金资助:
    国家自然科学基金项目; 江西省研究生创新专项资金项目

Evaluation of Node Importance in Opportunistic Networks Based on Node Embedding

LIU Linlan1, CUI Hui1, GAO Haoxuan2, SHU Jian2, JIANG Yunan1   

  • Received:2023-11-01 Revised:2024-01-18 Online:2025-02-26 Published:2025-02-25

摘要: 为提高机会网络节点重要度评估的准确性,提出了一种基于节点嵌入的节点重要度评估方法。针对机会网络的时变性,采用时间窗口聚合图得到网络拓扑数据和时序连接数据。采用图注意力机制提取节点的拓扑特征,应用时间编码和自注意力机制提取节点时序特征,融合两种特征获得节点嵌入向量。提出节点的聚类重要度,构建转移概率矩阵,结合PageRank算法得到节点重要度。在真实机会网络数据集上的实验结果表明,相较于f-PageRank和动态图卷积网络等对比方法,所提方法具有更高的评估准确性。

关键词: 机会网络, 节点重要度, 节点嵌入, 自注意力机制, 转移概率矩阵

Abstract: To improve the accuracy of node importance evaluation in opportunistic networks, a node embedding-based node importance evaluation method is proposed. Considering the time-varying nature of opportunistic networks, a time window aggregation graph is employed, so that network topology and temporal connection data can be obtained in the window. Graph attention mechanism is utilized to extract the topological features of nodes, and temporal encoding and self-attention mechanism are employed to capture temporal features of nodes. Node embedding vectors are achieved by integrating two features. The cluster importance for nodes is introduced and the transition probability matrix is constructed. The node importance is obtained by PageRank algorithm. On real opportunistic network datasets, experimental results demonstrate that the proposed method exhibits better evaluation accuracy compared to approaches such as f-PageRank and dynamic graph convolutional network.

Key words: opportunistic network ,  node importance ,  node embedding ,  self-attention mechanism ,  transition probability matrix

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