Predicting missing links via local feature of common neighbors

Predicting missing links in complex networks has a theoretical interest and practical significance in social network analysis. In this paper, we study the link prediction results as the change of the exponent on common neighbor´s degree and find some regular pattern between different networks and different exponent. Then we present a local network metric based on the regular pattern to estimate the likelihood of the existence of a link between two nodes. Our new metric takes the exponent of common neighbor´s degree into consideration for link prediction. The paper also recommends the value range of the exponent. We compare nine well-known local information metrics and the new metric on eight real networks. The result evaluated by AUC indicates that our new metric, namely Degree Exponent Change metric, have a better prediction accuracy than other well-known metrics.