  Dynamic graphs serve as a generic abstraction and description of the
evolutionary behaviors of various complex systems (e.g., social networks and
communication networks). Temporal link prediction (TLP) is a classic yet
challenging inference task on dynamic graphs, which predicts possible future
linkage based on historical topology. The predicted future topology can be used
to support some advanced applications on real-world systems (e.g., resource
pre-allocation) for better system performance. This survey provides a
comprehensive review of existing TLP methods. Concretely, we first give the
formal problem statements and preliminaries regarding data models, task
settings, and learning paradigms that are commonly used in related research. A
hierarchical fine-grained taxonomy is further introduced to categorize existing
methods in terms of their data models, learning paradigms, and techniques. From
a generic perspective, we propose a unified encoder-decoder framework to
formulate all the methods reviewed, where different approaches only differ in
terms of some components of the framework. Moreover, we envision serving the
community with an open-source project OpenTLP that refactors or implements some
representative TLP methods using the proposed unified framework and summarizes
other public resources. As a conclusion, we finally discuss advanced topics in
recent research and highlight possible future directions.
