Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Robust Graph Contrastive Learning for Incomplete Multi-view Clustering
Authors: Deyin Zhuang, Jian Dai, Xingfeng Li, Xi Wu, Yuan Sun, Zhenwen Ren
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. |
| Researcher Affiliation | Academia | Deyin Zhuang1 , Jian Dai2 , Xingfeng Li1 , Xi Wu1 , Yuan Sun3,4 , Zhenwen Ren1 1Southwest University of Science and Technology, China 2Southwest Automation Research Institute, China 3 College of Computer Science, Sichuan University, China 4 National Key Laboratory of Fundamental Algorithms and Models for Engineering Numerical Simulation, Sichuan University, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology in detail in Section 3, including Multi-view Reconstruction, Noise-robust Graph Contrastive Learning, Cross-view Graph-level Alignment, and Implementation, but it does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is available at https://github.com/DYZ163/RGCL.git. |
| Open Datasets | Yes | In this section, we evaluate the performance of the proposed method on the six multi-view datasets, including Hand Written [Le Cun et al., 1989], COIL20 [Nene et al., 1996], BDGP [Cai et al., 2012], Land Use-21 [Yang and Newsam, 2010], ALOI-100 [Geusebroek et al., 2005], and AWA [Romera Paredes and Torr, 2015]. |
| Dataset Splits | No | To evaluate the performance for incomplete multi-view data, we randomly set the instances with a certain ratio (i.e., [0.1, 0.3, 0.5, 0.7]) as the missing pairs. This describes the method for introducing incompleteness, but not the explicit training/test/validation splits for the datasets themselves. |
| Hardware Specification | Yes | For all experiments, we employ a Linux platform equipped with an NVIDIA RTX 4090 GPU and 32GB of memory |
| Software Dependencies | Yes | using Py Torch version 2.3.0. |
| Experiment Setup | Yes | To be specific, the view-specific encoder and decoder layers are configured with dimensions of (0.8dv, 0.8dv, 1500, C) and (C, 1500, 0.8dv, 0.8dv, dv), respectively. ... We set the temperature parameters to σ = 0.1 and θ = 0.05. ... the optimal value of λ,α and β, i.e. λ = 0.5, α=0.005 or 0.01, and β=0.005 or 0.01. |