A link prediction algorithm based on discrete-time quantum walk model on weighted graphs

Compared with the classical random walk, the quantum walk has the advantages of less time to find the target node and less time scattered from the source node to all nodes. Therefore, in order to further improve the accuracy of link prediction algorithms, a link prediction algorithm based on discrete-time quantum walk (DTQW) on weighted graphs is proposed by integrating node attributes and introducing the idea of assigning weights to edges in network dynamics. In this algorithm, a weighted graph is first constructed based on the attribute information of nodes in the graph, and then a DTQW model is constructed based on the weighted graph. Then, under the control of the evolution operator, the walker performs a DTQW on the weighted graph; Finally, the link prediction algorithm is implemented by defining the similarity between nodes based on the average probability of the walker reaching each node. The experimental results show that in three real social networks, Douban, Weibo and Zhihu, this algorithm has different degrees of improvement in prediction accuracy and the algorithm stability is among the top in comparison with the optimal benchmark prediction algorithm.