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With the fast development of transportation technology,the railway has become one of the main choices for people when they travel for business,vacation or visiting.As a result,the behavior of co-travel has become more and more common.Based on this co-travel relationship,people can construct a co-travel network,where each node represents a passenger and an edge indicates co-travel frequency between two passengers this edge connects,and the link prediction on the network such that persona-lized service and product can be provided even better.In light of this,this paper proposes a novel approach to predicting potential co-travel relationship.Specifically,we first propose two types of co-travel graph pattern association rules which are extended from their traditional counterparts,and can be used to predict new co-travel relationship and co-travel frequency,respectively.We then decompose this mining problem into three sub-problems,i.e.,frequent co-travel pattern mining,rules generation and association analysis,and develop parallel and centralized algorithms for these sub-problems.Extensive experimental studies on large real-life datasets show that our approach can predict potential co-travel relationship efficiently and accurately,with accuracies higher than 50% for two types of rules,and substantially superior to the traditional method (e.g.,Jaccard with accuracy 24%).