The evolutionary behavior of temporal networks has gained the attention of researchers with its ubiquitous applications in a variety of real-world scenarios. Learning evolutionary behavior of networks is directly related to link prediction problem, as the addition or removal of new links or edges over time leads to the network evolution. With the rise of large-scale temporal networks such as social networks, temporal link prediction has become an interesting field of study. In this work, we provide a detailed survey of various researches carried out in the direction of temporal link prediction. We build a taxonomy of temporal link prediction methods based on various approaches used and discuss the works which come under each category. Further, we present the challenges and directions for future works.

中文翻译：

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时间链接预测：调查

时间网络的进化行为因其在各种现实世界场景中的普遍应用而引起了研究人员的关注。学习网络的进化行为与链接预测问题直接相关，因为随着时间的推移添加或删除新链接或边会导致网络进化。随着社交网络等大规模时间网络的兴起，时间链接预测已成为一个有趣的研究领域。在这项工作中，我们详细调查了在时间链接预测方向上进行的各种研究。我们基于使用的各种方法构建了时间链接预测方法的分类，并讨论了每个类别下的作品。此外，我们提出了未来工作的挑战和方向。