基于位置的社交网络中基于时空关系的超网络链接预测方法

Supernetwork link prediction method based on spatio-temporal relation in location-based social network

针对基于位置的社交网络(LBSN)中因现有方法未能有效融合社会因素、位置因素以及时间因素的综合影响而导致链接预测准确度低的问题,提出了一种LBSN中基于时空关系的超网络链接预测方法。首先,针对LBSN中网络的异构性以及用户间的时空关系特性,将网络划分成"时空-用户-位置-类别"四层超网络,降低影响因素间的耦合性;其次,考虑到边权值对网络的影响,通过挖掘用户影响力、隐关联关系、用户偏好以及节点度信息,对子网的边权值进行定义和量化,构建四层加权超网络模型;最后,在加权超网络模型的基础上,定义超边及加权超边结构,挖掘用户之间的多元关联关系进行预测。实验结果表明,所提方法较基于同构和异构的链接预测方法在准确率、召回率、F1值以及AUC上具有一定的提升,其中AUC指标较基于异构的链接预测方法提升了4.69%。

The accuracy of link prediction in the existing methods for Location-Based Social Network（LBSN） is low due to the failure of integrating social factors, location factors and time factors effectively. In order to solve the problem, a supernetwork link prediction method based on spatio-temporal relation was proposed in LBSN. Firstly, aiming at the heterogeneity of network and the spatio-temporal relation among users in LBSN, the network was divided into four-layer supernetwork of ＂spatio-temporal-user-location-category＂ to reduce the coupling between the influencing factors. Secondly,considering the impact of edge weights on the network, the edge weights of subnets were defined and quantified by mining user influence, implicit association relationship, user preference and node degree information, and a four-layer weighted supernetwork model was built. Finally, on the basis of the weighted supernetwork model, the super edge as well as weighted super-edge structure were defined to mine the multivariate relationship among users for prediction. The experimental results show that, compared with the link prediction methods based on homogeneity and heterogeneity, the proposed method has a certain increase in accuracy, recall, F1-measure（F1） as well as Area Under the receiver operating characteristic Curve（AUC）, and its AUC index is 4. 69% higher than that of the link prediction method based on heterogeneity.