Exploiting Non-visible Relationship in Link Prediction Based on Asymmetric Local Random Walk

Abstract

Link prediction is an important aspect of complex network evolution analysis. In the existing link prediction algorithms, the sparseness and scale of the target network have a great influence on the prediction results, and the link prediction algorithm based on local random walk is better in solving this problem. However, the existing local random walk link prediction algorithm simplifiy the definition of random walk process between nodes as symmetrical relationship, and ignore the influence of non-visible factors on the relationship of information diffusion between nodes. In this paper, for the first time, we introduce asymmetry and non-visible relationship of the network to the link prediction problem. Exploiting the unequal diffusion weights in different directions resulted from different degrees, we propose an asymmetric local random walk (ALRW) algorithm. In addition, with non-visible relationship to calculate of the similarity index, we propose a grounded asymmetric local random walk (GALRW) algorithm on the basis of ALRW. Compared with existing advanced link prediction algorithms, thorough experiments on typical datasets show that GALRW achieves better performance in prediction accuracy.