Temporal networks have gained significant prominence in the past decade for modelling dynamic interactions within complex systems. A key challenge in this domain is Temporal Link Prediction (TLP), which aims to forecast future connections by analysing historical network structures across various applications including social network analysis. While existing surveys have addressed specific aspects of TLP, they typically lack a comprehensive framework that distinguishes between representation and inference methods. This survey bridges this gap by introducing a novel taxonomy that explicitly examines representation and inference from existing methods, providing a novel classification of approaches for TLP. We analyse how different representation techniques capture temporal and structural dynamics, examining their compatibility with various inference methods for both transductive and inductive prediction tasks. Our taxonomy not only clarifies the methodological landscape but also reveals promising unexplored combinations of existing techniques. This taxonomy provides a systematic foundation for emerging challenges in TLP, including model explainability and scalable architectures for complex temporal networks.

翻译：时序网络在过去十年中因建模复杂系统内的动态交互而获得显著关注。该领域的一个关键挑战是时序链路预测（TLP），其目标是通过分析历史网络结构来预测未来连接，应用涵盖社交网络分析等多个领域。尽管现有综述已探讨TLP的特定方面，但通常缺乏一个能明确区分表示方法与推断方法的综合框架。本综述通过引入一种新颖的分类法来弥合这一差距，该分类法从现有方法中明确考察表示与推断机制，从而为TLP方法提供了全新的分类体系。我们分析了不同表示技术如何捕捉时序与结构动态，并检验它们与各类推断方法在转导式和归纳式预测任务中的兼容性。该分类法不仅厘清了方法学格局，还揭示了现有技术中具有潜力的未探索组合。这一体系为TLP领域的新兴挑战（包括模型可解释性及复杂时序网络的可扩展架构）提供了系统性的理论基础。