End-to-End Open-Set Semi-Supervised Node Classification with Out-of-Distribution Detection
Authors: Tiancheng Huang, Donglin Wang, Yuan Fang, Zhengyu Chen
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various datasets show that the proposed method outperforms state-of-the-art baselines in terms of both node classification and OOD detection. |
| Researcher Affiliation | Academia | 1 Zhejiang University, Hangzhou, China 2 Westlake University, Hangzhou, China 3 Westlake Institute for Advanced Study, Hangzhou, China 4 Singapore Management University, Singapore |
| Pseudocode | No | The paper describes methods and frameworks but does not contain a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing code for the described methodology or a direct link to a source-code repository. |
| Open Datasets | Yes | For semi-supervised node classification, we employ four widely used benchmark datasets: 1) Cora; 2) Citeseer; 3) Pubmed; and 4) ogbn-ar Xiv [Hu et al., 2020]. |
| Dataset Splits | Yes | Following semi-supervised node classification setting [Rong et al., 2019], we apply the standard fixed training/validation/testing split, where 20 labeled nodes per class are for training, 500 nodes are for validation, and the remaining nodes are for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions implementing models in "Py Torch and Py Torch-Geometric" but does not specify version numbers for these software dependencies. |
| Experiment Setup | Yes | Following semi-supervised node classification setting [Rong et al., 2019], we apply the standard fixed training/validation/testing split, where 20 labeled nodes per class are for training, 500 nodes are for validation, and the remaining nodes are for testing. |