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..
Deep Graph Clustering via Dual Correlation Reduction
Authors: Yue Liu, Wenxuan Tu, Sihang Zhou, Xinwang Liu, Linxuan Song, Xihong Yang, En Zhu7603-7611
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on six benchmark datasets demonstrate the effectiveness of the proposed DCRN against the existing state-of-the-art methods. |
| Researcher Affiliation | Academia | 1College of Computer, National University of Defense Technology, Changsha, China 2College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China |
| Pseudocode | Yes | Algorithm 1: Dual Correlation Reduction Network |
| Open Source Code | Yes | The code of DCRN is available at https://github.com/yueliu1999/DCRN and a collection (papers, codes and, datasets) of deep graph clustering is shared at https://github.com/yueliu1999/Awesome-Deep Graph-Clustering on Github. |
| Open Datasets | Yes | To evaluate the effectiveness of the proposed method, we conduct extensive experiments on six widely-used datasets, including DBLP, CITE, ACM(Bo et al. 2020), AMAP, PUBMED, and CORAFULL(Shchur et al. 2018). |
| Dataset Splits | No | The paper describes the training procedure (pre-training, integrating subnetworks, training the whole network for certain epochs) but does not provide specific details on train/validation/test splits like percentages, sample counts, or explicit references to predefined splits. |
| Hardware Specification | Yes | The proposed DCRN is implemented with a NVIDIA 3090 GPU on Py Torch platform. |
| Software Dependencies | No | The paper mentions 'Py Torch platform' but does not provide a specific version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The learning rate is set to 1e-3 for AMAP, 1e-4 for DBLP, 5e-5 for ACM, 1e-5 for CITE, PUBMED, and CORAFULL, respectively. The hyper-parameters α is set to 0.1 for PUBMED and 0.2 for other datasets. Moreover, we set λ and γ to 10 and 1e3, respectively. K in Eq. 6 is set to the cluster number C. |