Online social networks (OSNs) analysis has been widely used in the field of information systems (IS), thus link prediction, one of the most important core techniques of OSNs analysis, plays a vital role in the development of IS. Despite the recent development of numerous link prediction approaches, there is still a lack of comprehensive studies that measure and evaluate their performance, which hinders the rational selection and full utilization of existing prediction approaches. This study proposes a novel taxonomy of link prediction approaches based on their prediction principles. Furthermore, it selects eighteen representative approaches from various categories to perform an empirical evaluation on six real-world benchmark datasets. The features of different types of predication approaches have been analyzed based evaluation test results. The research provides researchers with improved understandings on link prediction approaches and offers insightful performance related information to practitioners for developing more effective information systems.

在线社交网络（OSNs）分析已广泛应用于信息系统（IS）领域，链接预测作为OSNs分析最重要的核心技术之一，在IS的发展中发挥着至关重要的作用。尽管最近发展了许多链路预测方法，但仍然缺乏衡量和评估其性能的综合研究，这阻碍了现有预测方法的合理选择和充分利用。这项研究根据其预测原理提出了一种新的链接预测方法分类法。此外，它从不同类别中选择了十八种代表性方法，对六个现实世界基准数据集进行实证评估。根据评价测试结果分析了不同类型预测方法的特点。该研究使研究人员更好地理解了链接预测方法，并为从业者提供了富有洞察力的性能相关信息，以开发更有效的信息系统。