# Temporal Link Prediction: A Unified Framework, Taxonomy, and Review

arXiv: https://arxiv.org/abs/2210.08765 · HTML: https://mlsanity.com/paper/2210.08765/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2022.

## Built on by (most cited first, 20 of 20)

- [Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities](https://mlsanity.com/paper/2302.01018/) (2023)
- [Improving Temporal Link Prediction via Temporal Walk Matrix Projection](https://mlsanity.com/paper/2410.04013/) (2024)
- [A Survey of Link Prediction in Temporal Networks](https://mlsanity.com/paper/2502.21185/) (2025)
- [Machine Learning on Dynamic Graphs: A Survey on Applications](https://mlsanity.com/paper/2401.08147/) (2024)
- [A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunities](https://mlsanity.com/paper/2505.04461/) (2025)
- [Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspective](https://mlsanity.com/paper/2608.18104/) (2026)
- [Understanding and Improving Laplacian Positional Encodings For Temporal GNNs](https://mlsanity.com/paper/2506.01596/) (2025)
- [Towards Faster Graph Partitioning via Pre-training and Inductive Inference](https://mlsanity.com/paper/2409.00670/) (2024)
- [Between Linear and Sinusoidal: Rethinking the Time Encoder in Dynamic Graph Learning](https://mlsanity.com/paper/2504.08129/) (2025)
- [Efficient Identity and Position Graph Embedding via Spectral-Based Random Feature Aggregation](https://mlsanity.com/paper/2505.20992/) (2025)
- [Transfer Learning for Temporal Link Prediction](https://mlsanity.com/paper/2504.10925/) (2025)
- [InfraredGP: Efficient Graph Partitioning via Spectral Graph Neural Networks with Negative Corrections](https://mlsanity.com/paper/2508.19737/) (2025)
- [Semantic Random Walk for Graph Representation Learning in Attributed Graphs](https://mlsanity.com/paper/2305.06531/) (2023)
- [Multi-Class and Multi-Task Strategies for Neural Directed Link Prediction](https://mlsanity.com/paper/2412.10895/) (2024)
- [Evaluating link prediction: New perspectives and recommendations](https://mlsanity.com/paper/2502.12777/) (2025)
- [PTCL: Pseudo-Label Temporal Curriculum Learning for Label-Limited Dynamic Graph](https://mlsanity.com/paper/2504.17641/) (2025)
- [MUST: Multi-Scale Structural-Temporal Link Prediction Model for UAV Ad Hoc Networks](https://mlsanity.com/paper/2505.09331/) (2025)
- [ST-TGExplainer: Disentangling Stability and Transition Patterns for Temporal GNN Interpretability](https://mlsanity.com/paper/2605.19822/) (2026)
- [TGFormer: Towards Temporal Graph Transformer with Auto-Correlation Mechanism](https://mlsanity.com/paper/2605.24971/) (2026)
- [Topology-induced Operators Reveal Complementary Graph Representations without Training](https://mlsanity.com/paper/2609.08152/) (2026)

## Abstract (arXiv)

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.