Link prediction methods anticipate the likelihood of a future connection between two nodes in a given network. The methods are essential in social networks to infer social interactions or to suggest possible friends to the users. Rapid social network growth trigger link prediction analysis to be more challenging especially with the significant advancement in complex social network modeling. Researchers implement numerous applications related to link prediction analysis in different network contexts such as dynamic network, weighted network, heterogeneous network and cross network. However, link prediction applications namely, recommendation system, anomaly detection, influence analysis and community detection become more strenuous due to network diversity, complex and dynamic network contexts. In the past decade, several reviews on link prediction were published to discuss the algorithms, state-of-the-art, applications, challenges and future directions of link prediction research. However, the discussion was limited to physical domains and had less focus on social network perspectives. To reduce the gap of the existing reviews, this paper aims to provide a comprehensive review and discuss link prediction applications in different social network contexts and analyses, focusing on social networks. In this paper, we also present conventional link prediction measures based on previous researches. Furthermore, we introduce various link prediction approaches and address how researchers combined link prediction as a base method to perform other applications in social networks such as recommender systems, community detection, anomaly detection and influence analysis. Finally, we conclude the review with a discussion on recent researches and highlight several future research directions of link prediction in social networks.

链路预测方法可预测给定网络中两个节点之间未来连接的可能性。该方法在社交网络中对于推断社交互动或向用户推荐可能的朋友至关重要。快速的社交网络增长触发了链接预测分析，尤其是随着复杂的社交网络建模的显着进步，这更具挑战性。研究人员在动态网络，加权网络，异构网络和跨网络等不同网络环境中实现了许多与链路预测分析相关的应用程序。然而，由于网络多样性，复杂和动态的网络环境，链接预测应用程序，即推荐系统，异常检测，影响分析和社区检测变得更加费劲。在过去的十年中 发表了一些有关链接预测的评论，以讨论链接预测研究的算法，最新技术，应用，挑战和未来方向。但是，讨论仅限于物理领域，而很少关注社交网络的观点。为了减少现有评论的差距，本文旨在提供全面的评论并讨论链接预测在不同社交网络上下文和分析中的应用，重点是社交网络。在本文中，我们还基于先前的研究提出了常规的链接预测方法。此外，我们介绍了各种链接预测方法，并探讨了研究人员如何将链接预测作为一种基本方法，以在社交网络中执行其他应用程序，例如推荐系统，社区检测，异常检测和影响分析。最后，我们通过对最新研究的讨论来结束本综述，并重点介绍社交网络中链接预测的未来研究方向。