Meta-path based heterogeneous combat network link prediction
Meta-path based heterogeneous combat network link prediction
复制标题
基于元路径的异构作战网络链路预测
DOI：
10.1016/j.physa.2017.04.126
复制
发表时间：
2017-09-15
影响因子：
3.3
通讯作者：
Tan, Yuejin
中科院分区：
文献类型：
--
作者：
Li, Jichao;Ge, Bingfeng;Tan, Yuejin
高技术信息化战争中的“系统的系统”作战系统是由许多相互关联的不同类型的作战系统组成的，可以看作是一种复杂的异构网络。异构作战网络（HCNs）的链路预测具有重要的军事价值，因为它有助于重新配置作战网络，以便根据观察到的信息适当地表示复杂的现实世界网络拓扑结构。本文提出了一种新的基于元路径的HCN链路预测方法框架——HCNMP (HCN link prediction based meta-path)，用于同时预测HCN的多种类型的链路。更具体地说，引入了HCN元路径的概念，HCNMP可以通过提取所有六种定义类型的HCN链接的不同特征来积累信息。其次，建立基于元路径特征的HCN链路预测模型，同时预测HCN的所有类型的链路。然后，提出了HCN链路预测模型的求解算法，利用新的预测结果迭代更新得到预测结果，直到HCN中的结果收敛或达到一定的最大迭代次数。最后，在一个真实HCN数据集上进行了数值实验，并与30种基线方法进行了比较，验证了所提HCNMP方法的可行性和有效性。结果表明，HCNMP的性能优于基线方法。(C) 2017 Elsevier B.V.版权所有
The combat system-of-systems in high-tech informative warfare, composed of many interconnected combat systems of different types, can be regarded as a type of complex heterogeneous network. Link prediction for heterogeneous combat networks (HCNs) is of significant military value, as it facilitates reconfiguring combat networks to represent the complex real-world network topology as appropriate with observed information. This paper proposes a novel integrated methodology framework called HCNMP (HCN link prediction based on meta-path) to predict multiple types of links simultaneously for an HCN. More specifically, the concept of HCN meta-paths is introduced, through which the HCNMP can accumulate information by extracting different features of HCN links for all the six defined types. Next, an HCN link prediction model, based on meta-path features, is built to predict all types of links of the HCN simultaneously. Then, the solution algorithm for the HCN link prediction model is proposed, in which the prediction results are obtained by iteratively updating with the newly predicted results until the results in the HCN converge or reach a certain maximum iteration number. Finally, numerical experiments on the dataset of a real HCN are conducted to demonstrate the feasibility and effectiveness of the proposed HCNMP, in comparison with 30 baseline methods. The results show that the performance of the HCNMP is superior to those of the baseline methods. (C) 2017 Elsevier B.V. All rights reserved.