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 | Conference PDF | Archive PDF | Plain Text | 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. |