Is Megamerger Better?–Based on the Link Prediction Perspective

Abstract

Link prediction provides insight into the evolutionary mechanisms of complex networks by predicting missing edges. Existing research has proposed many similarity algorithms based on local information, and some link prediction algorithms typically perform better in different networks. It is generally believed that a megamerger is beneficial. In the perspective of link prediction, merging the good-performing algorithms brings higher prediction accuracy. And the more times the experiment is executed, the higher the accuracy of link prediction. Therefore, this research proposes a new link prediction algorithm based on the theory of megamerger in management and the concept of partnership, and uses ten actual complex networks for experiments to test the above two hypotheses. The experimental results show that megamerger is not applicable to the link prediction algorithm. In addition, there is no positive correlation between the increasing the quantity of experiments and improving the accuracy of the experiments, so the above two hypotheses are rejected. Hence, this research presumes that megamerger of the comprehensive information of the network, such as the resource flow between nodes, the degree of common neighbor nodes, and partnership of nodes, does not improve the accuracy of link prediction. For a refined network with a small number of nodes and a short average path length, it is recommended that the quantity of experiments be set to only ten can achieve the required accuracy of link prediction.