Abstract: The data form of network can express the entity and the relation between entity and entity.Network structure is common in the real world.It is great significance to study the relationship between nodes and edges in networks.Network representation technology transforms the structure information of network into node vector,which can reduce the complexity of graph representation,and can be effectively applied to tasks such as classification,network reconstruction and link prediction.The SDNE (structural deep network embedding) algorithm proposed in recent years has made outstanding achievements in the field of graph auto-encoder.In view of the limitations of SDNE in weighted and directed networks,this paper proposed a new network representation model based on graph auto-encoder from the perspectives of network structure and measurement index.The concepts of receiving and sending vector are introduced to optimize the decoding part of the neural network,which reduce the para-meters of the network to speed up the convergence speed.This paper proposed a measurement index based on the node degree,and reflected the weighted characteristics of the network in the results of the network representation.Experiments on three directed weighted datasets show that the proposed method can achieve better results than the traditional method and the original SDNE method in network reconstruction and link prediction tasks.

Key words: Auto-encoder, Complex network, Link prediction, Network reconstruction, Network representation