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 [1].
GraphSR: A Data Augmentation Algorithm for Imbalanced Node Classification
Authors: Mengting Zhou, Zhiguo Gong
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate the proposed approach outperforms the state-of-the-art baselines on various class-imbalanced datasets. |
| Researcher Affiliation | Academia | Mengting Zhou1,2, Zhiguo Gong1,2* 1State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao 2 Guangdong-Macau Joint Laboratory for Advanced and Intelligent Computing |
| Pseudocode | No | The paper describes the methodology verbally and with equations but does not include any explicitly labeled or formatted pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing code or a link to a source code repository for the described methodology. |
| Open Datasets | Yes | We evaluate Graph SR on several widely-used public datasets for node classification task: Cora, Cite Seer, Pub Med for citation networks (Sen et al. 2008). |
| Dataset Splits | Yes | All majority classes maintain 20 nodes in the training set, and the numbers for minority classes are 20 ρ, where ρ is the imbalanced ratio. When validating and testing, we sample the same numbers of nodes for all classes to make the validation and test set balanced. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions models and techniques (GNNs, GCN, Graph SAGE, PPO, MLP) but does not provide specific software library names with version numbers (e.g., PyTorch 1.9, TensorFlow 2.x) that are needed to replicate the experiment. |
| Experiment Setup | No | The paper discusses general experimental settings like the imbalance ratio and components of the proposed method but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs, optimizer settings) or detailed system-level training configurations. |