A Survey of Link Prediction in Temporal Networks

TL;DR

This survey comprehensively reviews temporal link prediction in dynamic networks, introducing a new taxonomy that distinguishes representation and inference methods, and highlights promising unexplored combinations for future research.

Contribution

It provides a novel taxonomy for TLP approaches, explicitly separating representation and inference methods, and analyzes their compatibility for transductive and inductive tasks.

Findings

Introduces a new taxonomy for TLP methods

Analyzes compatibility of representation and inference techniques

Identifies promising unexplored method combinations

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 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.