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Predicting links in complex networks has been one of the essential topicswithin the realm of data mining and science discovery over the past few years.This problem remains an attempt to identify future, deleted, and redundantlinks using the existing links in a graph. Local random walk is considered tobe one of the most well-known algorithms in the category of quasi-localmethods. It traverses the network using the traditional random walk with alimited number of steps, randomly selecting one adjacent node in each stepamong the nodes which have equal importance. Then this method uses thetransition probability between node pairs to calculate the similarity betweenthem. However, in most datasets, this method is not able to perform accuratelyin scoring remarkably similar nodes. In the present article, an efficientmethod is proposed for improving local random walk by encouraging random walkto move, in every step, towards the node which has a stronger influence.Therefore, the next node is selected according to the influence of the sourcenode. To do so, using mutual information, the concept of the asymmetric mutualinfluence of nodes is presented. A comparison between the proposed method andother similarity-based methods (local, quasi-local, and global) has beenperformed, and results have been reported for 11 real-world networks. It had ahigher prediction accuracy compared with other link prediction approaches.

A Preference Random Walk Algorithm for Link Prediction through Mutual Influence Nodes in Complex Networks