Design of link prediction algorithm for complex network based on the comprehensive influence of predicting nodes and neighbor nodes

Abstract

A new link prediction algorithm (ZHA), based on comprehensive influence of predicting nodes and neighbor nodes to improve the accuracy and applicability of link prediction for complex networks, was proposed. Taking the comprehensive influence of predicting nodes and neighbor nodes into account, the new algorithm was constructed on the basis of the information of nodes in complex networks. ZHA was applied to seven real complex networks, and the random experiment was performed 10 times and 100 times, respectively, to identify its applicability and precision. Comparing the precision of ZHA with classical similarity link prediction algorithms, results showed that the new algorithm ZHA had higher precision. On the foundation of the experiments, the relationship between the accuracy of link prediction and experiment times was analyzed, and the principle of how to select experiment times was given.

Link Prediction Model for Weighted Networks Based on Evidence Theory and the Influence of Common Neighbours

“…Based on this definition, the DS evidence theory is used to fuse the similarity contribution based on CCNs and FCNs. In this paper, the identification framework of seed node pair <x, y> based on the evidence theory is denoted as {m x,y , m x,y }, where m x,y represents the probability of a link existing between nodes x and y, and its definition is shown in formula (16); m x,y represents the probability that there is no link between nodes x and y, and its definition is shown in formula (17). e new fusion weighted similarity index is S CCNI−FCNI DS x,y , as shown in formulas (18) and (19).…”

“…Moreover, Li et al [15] presented a method based on a topologically valid connected path, which quantified the local influence of nodes and realised link prediction in directed networks. To achieve high prediction accuracy and applicability, Wang et al [16] constructed an algorithm based on the combined effect of the predicted nodes and theirs neighbours. By introducing parameters to adjust the link effect between neighbours and paths, Li et al [17] proposed a prediction algorithm based on relative paths.…”