基于可靠性增强的社交网络链接预测算法OA

The link prediction(LP)for social network is an important research direction of complex network mining,which tries to detect the potential relationship between network nodes.The popular LP algorithms for social networks include the static similarity-based indicators,dynamic learning-based predictors and content-based methods.Although the experimental results have reported the good performances in specific application scenarios of these algorithms,some essential defects such as prediction unreliability and detection incapability still exist and cannot be effectively solved up to now.Therefore,a reliability enhancement-based link prediction(RE-LP)algorithm is proposed to make up the above-mentioned shortcomings of existing LP algorithms.There are three main components in RE-LP algorithm,i.e.,non-existing link partition,reliable predictor construction,and connection probability calculation.In RE-LP algorithm,the observable non-existing links are sophisticatedly partitioned into highly-reliable non-existing(HRNE)links and possibly-observed existing(POE)links.It then continuously identifies HRNE links from the POE links in an iterative manner,and uses a well-performed Bayesian classifier-based link predictor to calculate the connection probabilities of the POE links,in order to achieve the goal of accurately and reliably predicting unknown links.Through a series of verification experiments,the advantages of RE-LP algorithm in terms of feasibility,rationality and effectiveness are fully demonstrated.The experimental results demonstrate that RE-LP algorithm can obtain the 21.7%～36.1%higher prediction AUC than 5 advanced LP algorithms and meanwhile is able to detect the potential links with high credibility.