Combat network link prediction based on embedding learning

Link prediction of combat networks is of significant military value for precisely identifying the vital infrastructure of the enemy target and optimizing the operational plan of our side. Due to the profound uncertainty in the battleground circum-stances, the acquired topological information of the opponent combat network always presents sparse characteristics. To solve this problem, a novel approach named network embed-ding based combat network link prediction (NECLP) is put for-ward to predict missing links of sparse combat networks. First, node embedding techniques are presented to preserve as much information of the combat network as possible using a low-di-mensional space. Then, we put forward a solution algorithm to predict links between combat networks based on node embed-ding similarity. Last, massive experiments are carried out on a real-world combat network case to verify the validity and prac-ticality of the proposed NECLP. This paper compares six baseline methods, and experimental results show that the NECLP has outstanding performance and substantially outper-forms the baseline methods.