Abstract: Network embedding aims to embed each vertex into a low dimensional vector space and preserves certain structural relationships among the vertices in the networks.However,in the directed networks,vertexes can be reached by each other if they are in the same circle,which damages asymmetric transitivity preservation and makes representation learning model hard to capture global information of complex directed networks.This paper proposes an improved representation learning model for directed networks,which weakens the influence of circles in representation learning and enhances the ability of model to obtain global structure information.The proposed method uses TrueSkill to inference hierarchy of a directed graph and compute weight of each edge using hierarchy information.At last,this paper applies this method to some existing embedding models,and then conducts experiments on tasks of link prediction and node classification on several open source datasets.Experimental results show that the proposed method is highly scalable and effective.

Key words: Directed network, Hierarchical Structure, Link prediction, Representation learning