Heterogeneous Combat Network Link Prediction Based on Representation Learning

Integrated joint operations under the conditions of information make the battlefield environment increasingly complex and with great uncertainty. Link prediction for combat networks is of significant military values for improving the understanding of complicated battlefield situations, helping to identify key enemy targets, and optimizing combat plans. This article proposes a method called heterogeneous combat network link prediction based on representation learning (RLHCN) to predict multiple types of links in the combat networks. First, a heterogeneous combat network (HCN) model is built by abstracting diversified systems and information flows of the combat system of systems (CSoS) into multiple types of nodes and edges. Next, taking into the sparse feature of HCNs into consideration, we proposed a network representation learning-based link prediction method and a solution algorithm to predict links in HCNs. Then, a real-world combat network is taken as an example and extensive experiments are conducted to demonstrate the feasibility and effectiveness of the proposed method. The results show that RLHCN is superior to the baseline methods and can achieve good performance in predicting multiple types of links in HCN.