A negative link prediction method for signed social networks

Abstract

Aiming at the disadvantages of negative link feature fusion and effective information mining in signed social networks, resulting in low prediction performance, a new feature fusion negative link prediction method is proposed. Based on the classic structural balance theory and social status theory, this method constructed four features related to negative signs, including node feature, structural feature, similarity feature, and scoring feature, and used logistic regression algorithms to realize negative links prediction. Its effectiveness was verified on Epinions and Slashdot two typical signed network data sets. Experimental results show that compared with the benchmark method, the accuracy of this method is increased by about 4.5% and 10.4% on the two data sets, respectively, and the F1 score is increased by about 27.3% and 31.5%, which achieve the goal of improving the effect of negative link prediction.

Keywords

• signed network
• social network
• link prediction
• negative relationship
• feature fusion