---
title: "Uncertainty-guided Graph Contrastive Learning from a Unified Perspective"
authors:
  - "Zhiqiang Li"
  - "Jie Wang"
  - "Jianqing Liang"
  - "Junbiao Cui"
  - "Xingwang Zhao"
  - "Jiye Liang"
doi: "10.24963/ijcai.2025/629"
url: "https://ijcai.org/proceedings/2025/629"
pdf: "https://ijcai.org/proceedings/2025/0629.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "5653-5661"
track: "Main Track"
keywords:
  - "Data Mining: DM: Mining graphs"
  - "Machine Learning: ML: Self-supervised Learning"
---

# Uncertainty-guided Graph Contrastive Learning from a Unified Perspective

Zhiqiang Li, Jie Wang, Jianqing Liang, Junbiao Cui, Xingwang Zhao, Jiye Liang

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 5653-5661. DOI: [10.24963/ijcai.2025/629](https://doi.org/10.24963/ijcai.2025/629)

## Abstract

The success of current graph contrastive learning methods largely relies on the choice of data augmentation and contrastive objectives. However, most existing methods tend to optimize these two components independently, neglecting their potential interplay, which leads to suboptimal quality of the learned embeddings. To address this issue, we propose Uncertainty-guided Graph Contrastive Learning (UGCL) from a unified perspective. The core of our method is the introduction of sample uncertainty, a critical metric that quantifies the degree of class ambiguity within individual samples. On this basis, we design a novel multi-scale data augmentation strategy and a weighted graph contrastive loss function, both of which significantly enhance the quality of embeddings. Theoretically, we demonstrate that UGCL can coordinate overall optimization objectives through uncertainty, and through experiments, we show that it improves the performance of tasks such as node classification, node clustering, and link prediction, thereby verifying the effectiveness of our method.