An Index Hybrid Method Based on Improved Logistic Model for Link Prediction

Abstract

Abstract. In link prediction, the single index prediction effect depends on whether the method can reasonably describe the target network topology characteristics. What's more, the network features are only described from a certain perspective, this limitation leads to the low robustness of a single index for the prediction of different networks. Based on this, we proposed a hybrid method based on Logistic model, considering the similarity index of the prediction results of complementary characteristics and importance in different networks, adaptive fusion gives each index weight reasonably. Experiments show that the AUC and Precision of the hybrid method on each target network are higher than those of the baseline.