Link Prediction through Ensemble Techniques

Abstract

Link prediction is significant in the field of network analysis which helps to determine the future or hidden connections between the entities in a network. Traditional link prediction methods typically utilize single algorithms to make predictions, which might not be optimal for diverse and complex networks. In this research paper, a new framework is proposed which utilizes the existing link prediction algorithms with ensemble techniques. We have explored three ensemble strategies: bagging, boosting, and voting classifier and four individual link prediction algorithms: Adamic Adar, Katz, CCPA, Accurate link prediction. Methods considering ensemble techniques combine multiple individual models to improve prediction accuracy and robustness. We have experimentally compared the performance of proposed framework using six real world datasets in terms of AUC and Accuracy. The results show the superiority of the proposed framework over individual link prediction algorithms.