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..
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 | Venue PDF | 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. |