Collaborative Graph Convolutional Networks: Unsupervised Learning Meets Semi-Supervised Learning
Authors: Binyuan Hui, Pengfei Zhu, Qinghua Hu4215-4222
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on benchmark graph datasets validate the superiority of our proposed GMM-VGAE compared with the state-of-the-art attributed graph clustering networks. The performance of node classification is greatly improved by our proposed CGCN, which verifies graph-based unsupervised learning can be well exploited to enhance the performance of semisupervised learning. |
| Researcher Affiliation | Academia | Binyuan Hui, Pengfei Zhu, Qinghua Hu College of Intelligence and Computing Tianjin University, Tianjin, China {huibinyuan, zhupengfei, huqinghua}@tju.edu.cn |
| Pseudocode | Yes | Algorithm 1: CGCN |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology. |
| Open Datasets | Yes | Cora, Citeseer and Pubmed (Sen et al. 2008) are citation networks where the number of nodes varies from 2708 to 19717 and the number of feature varies from 500 to 3703. |
| Dataset Splits | No | For each run, we split the data into one small sample subset for training, and the test sample subset with 1000 samples. The paper explicitly mentions a test split but does not specify details for a separate validation split, only referring to baselines using validation. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We train GMM-VGAE module with Adam learning algorithm (the learning rate is set as 0.01) for all datasets. We construct encoder using a two-layer GCN with 32 and 16 filters respectively, and initialize encoder weights as described in (Glorot and Bengio 2010). [...] We set the number of pretrain iterations Tp as 200, the number of retrain iterations Tr as 20 and the number of query high confidence nodes q as 20 for each pseudo-label assignment with T = 5 times. Following (Kipf and Welling 2017), we set the learning rate, dropout rate, regularization weight, and the size of second hidden layer as 0.01, 0.2, 0.5 10 4 and 16, respectively. |