Abstract: Network embedding algorithms embed a network into a low-dimensional vector space where the structure and the inherent properties of the graph can be preserved to the greatest extent.Compared with undirected networksdirected networks have special asymmetric transitivity which can be reflected in the high-order similarity measurement between nodes.A hot spot and difficulty of current directed network embedding research is how to preserve this feature well.Aiming at this problemthis paper introduces the co-citation network of directed networks and designs a metric function of the co-introduction information.At the same timea unified framework is created for fusing the co-citation information and the high-order similarity metrics of directed networks.Thenthis paper proposes a co-citation enhancing high-order proximity preserved embedding methodcalled CCE-HOPEwhich can preserve the asymmetric transitivity well.In experimentsthe proposed model is evaluated on link prediction using four real data sets.The results show that under different high-order similarity metricsthe performance of different proportions of co-introduction information follows a general regularityso the optimal range of the proportion can be determined.Compared with other state-of-the-art methodsthe method can effectively improve the accuracy of link prediction when the proportion of co-introduction information is within the optimal range.

Key words: Asymmetric transitivity, Co-citation network, Directed network embedding, Link prediction