A Survey of Link Prediction in Temporal Networks

Abstract 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 a range of applications. While existing surveys have addressed specific aspects of TLP, they typically lack a comprehensive framework that distinguishes between representation and inference methods, the two key ingredients of TLP. This survey bridges this gap by introducing a novel taxonomy that explicitly examines representation and inference in 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 link 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 tackling emerging challenges in TLP, including model explainability and scalable architectures for complex temporal networks.