---
title: "Learnable Data Augmentation and Contrastive Pre-training for Temporal Link Prediction"
authors:
  - "Canghong Jin"
  - "Jiafeng Zhao"
  - "Feng Xu"
  - "Tongya Zheng"
  - "Zemin Liu"
  - "Lina Wei"
  - "Mingli Song"
doi: "10.24963/ijcai.2026/303"
url: "https://ijcai.org/proceedings/2026/303"
pdf: "https://ijcai.org/proceedings/2026/0303.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "2726-2734"
track: "Main Track"
keywords:
  - "Data Mining: Mining graphs"
  - "Data Mining: Mining heterogenous data"
---

# Learnable Data Augmentation and Contrastive Pre-training for Temporal Link Prediction

Canghong Jin, Jiafeng Zhao, Feng Xu, Tongya Zheng, Zemin Liu, Lina Wei, Mingli Song

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 2726-2734. DOI: [10.24963/ijcai.2026/303](https://doi.org/10.24963/ijcai.2026/303)

## Abstract

Link prediction is a foundational task in temporal graphs. While temporal graph neural networks exhibit commendable performance, they are often criticized for providing inadequate representations, especially under limited data. Contrastive learning has been introduced as a solution for graph pre-training to mitigate data scarcity. However, the data augmentation techniques on which they depend frequently lead to suboptimal augmentations, manifesting either as over-augmentation or under-augmentation.
In light of these challenges, we explore the under-explored domain of contrastive learning of temporal graph transformers and propose a novel model, ContraTGT, which employs a dual-view graph transformer. This transformer is fine-tuned to extract sequences tailored for specific target nodes, encapsulating both spatial and temporal perspectives. To optimize the contrastive learning of this dual-view transformer, we put forth an innovative, learnable data augmentation method. This technique, which involves selective masking of elements within dual-view sequences, generates superior augmentations, thereby amplifying the potency of the contrastive learning approach.
Extensive experiments on five public temporal network datasets demonstrate that our model can consistently outperform all baselines, especially in small training set conditions.