Abstract
For social network with no rights and no directions, link prediction has lower computational complexity and higher link prediction accuracy by using similarity metrics. The existing local information-based similarity metrics are mostly calculated by the common neighbors of the pair of nodes to be predicted when performing link prediction, but these methods do not consider the influence of the relationship between the common neighbors. Aiming at this problem, this paper proposes a new link prediction algorithm based on the existing local information-based similarity measurement method, which not only considers the number and degree of common neighbors between the pairs of nodes to be predicted, but also considers the relationship between common neighbors. In order to verify the effectiveness of the algorithm, the proposed algorithm is compared with the original algorithm and other methods based on the similarity measure of local information in this paper. The experimental results demonstrate the effectiveness of the algorithm.
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