Link prediction based on the mutual information with high-order clustering structure of nodes in complex networks

Abstract

In complex networks, link prediction can forecast missing links and identify spurious interactions has wide applications in the real world. Although the high-order structure plays a vital role in network evolution, its effect on link prediction is not always taken into consideration in traditional prediction algorithms. In this paper, we come up with a novel link prediction approach based on Mutual information of the High-Order Clustering structure (MHOC). The MHOC approach integrates the effects of multiple higher-order structures of nodes and quantifies the different contributions of common neighbors with the aid of the diverse high-order clustering coefficients of nodes based on information entropy for predicting missing links. Experimental results demonstrate that in the real world network, the high-order clustering patterns of nodes can improve link prediction accuracy significantly.