Abstract:

Link prediction is a branch of complex network analysis. It predicts future linkage between nodes according to historical network structure information in order to reveal the network evolution and diffusion. This paper introduces a novel feature based on differentiated node contribution and provides seven supervised learning frameworks to carry the classification task via our introduced features and two categories of classic topological features which include node information and common neighbors’ property. The experimental results on eight realworld networks demonstrate interesting finding on the prediction influence by different classification models and topological diverse features via precision and ROC curve. In addition, we prove that our newly introduced features outperform the classic ones, which can mine the latent network information and enhance accuracy of link prediction.

Key words: link prediction;complex network;feature selection;classification;common neighbors