---
title: "Robust Graph Contrastive Learning for Incomplete Multi-view Clustering"
authors:
  - "Deyin Zhuang"
  - "Jian Dai"
  - "Xingfeng Li"
  - "Xi Wu"
  - "Yuan Sun"
  - "Zhenwen Ren"
doi: "10.24963/ijcai.2025/810"
url: "https://ijcai.org/proceedings/2025/810"
pdf: "https://ijcai.org/proceedings/2025/0810.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "7282-7290"
track: "Main Track"
keywords:
  - "Machine Learning: ML: Multi-view learning"
  - "Machine Learning: ML: Clustering"
  - "Machine Learning: ML: Multi-modal learning"
  - "Machine Learning: ML: Unsupervised learning"
---

# Robust Graph Contrastive Learning for Incomplete Multi-view Clustering

Deyin Zhuang, Jian Dai, Xingfeng Li, Xi Wu, Yuan Sun, Zhenwen Ren

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 7282-7290. DOI: [10.24963/ijcai.2025/810](https://doi.org/10.24963/ijcai.2025/810)

## Abstract

In recent years, multi-view clustering (MVC) has become a promising approach for analyzing heterogeneous multi-source data. However, during the collection of multi-view data, factors such as environmental interference or sensor failure often lead to the loss of view sample data, resulting in incomplete multi-view clustering (IMVC). Graph contrastive IMVC has demonstrated promising performance as an effective solution, which typically utilizes in-graph instances as positive pairs and out-of-graph instances as negative pairs. However, the construction of positive and negative pairs in this paradigm inevitably leads to graph noise Correspondence (GNC). To this end, we propose a new IMVC framework, namely robust graph contrastive learning (RGCL). Specifically, RGCL first completes the missing data by using a multi-view consistency transfer relationship graph. Then, to mitigate the impact of false negative pairs from graph contrastive, we propose noise-robust graph contrastive learning to mine intra-view consistency accurately. Finally, we present cross-view graph-level alignment to fully exploit the complementary information across different views. Experimental results on the six multi-view datasets demonstrate that our RGCL exhibits superiority and effectiveness compared with 9 state-of-the-art IMVC methods. The source code is available at https://github.com/DYZ163/RGCL.git.