Abstract: Link prediction is a research direction that has attracted much attention in the field of network analysis and mining.The link prediction algorithm predicts the missing connections in the network,which is actually a process of data mining,and the inferred possible future connections are related to the development and evolution of the network.Therefore,how to improve the accuracy of link prediction is a meaningful and challenging research.Based on the latest research on ego network decomposition and community clustering,a link prediction algorithm (Ego-Embedding) is proposed,which is based on ego network structure characteristics and network representation learning.Ego-Embedding converts the original network into a persona graph,and then reconstructs the embedding process by combining the microstructure information and context information of the network,learning one or more vector representations for each node,so that the vector representation can describe the node information more accurately,thereby improving the accuracy of link prediction.This paper conducts experimental simulations on three public data sets (Facebook,PPI-Yeast,ca-HepTh),and uses AUC as the evaluation index.The experimental results show that the performance of the algorithm Ego-Embedding is better than the five experimental comparison methods (CN,AA,node2vec,M-NMF,Splitter),and the best link prediction AUC reduces the error by up to 47%.

Key words: Ego network, Ego-Embedding, Link prediction, Network representation learning, Persona decomposition