Uncertainty Aware Semi-Supervised Learning on Graph Data
Authors: Xujiang Zhao, Feng Chen, Shu Hu, Jin-Hee Cho
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validated the outperformance of our proposed model compared to the state-of-the-art counterparts in terms of misclassification detection and OOD detection based on six real network datasets. |
| Researcher Affiliation | Academia | 1The University of Texas at Dallas, {xujiang.zhao, feng,chen}@utdallas.edu 2University at Buffalo, SUNY, shuhu@buffalo.edu 3Virginia Tech, jicho@vt.edu |
| Pseudocode | No | The paper describes methods through text and equations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code and datasets are accessible at https://github.com/zxj32/uncertainty-GNN |
| Open Datasets | Yes | We used six datasets, including three citation network datasets [17] (i.e., Cora, Citeseer, Pubmed) and three new datasets [20] (i.e., Coauthor Physics, Amazon Computer, and Amazon Photo). |
| Dataset Splits | No | The paper refers to 'training nodes' and 'testing nodes' and mentions OOD categories, but it does not provide specific percentages or absolute counts for training, validation, or test splits in the main text. It defers some setup details to an appendix. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions implementing models based on GCN but does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions or library versions). |
| Experiment Setup | No | The paper describes the loss function and optimization problem with trade-off parameters but does not provide concrete hyperparameter values (e.g., learning rate, batch size, specific values for λ1, λ2, or σ) in the main text, instead referring to an appendix for setup details. |