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].
IntraMix: Intra-Class Mixup Generation for Accurate Labels and Neighbors
Authors: Shenghe Zheng, Hongzhi Wang, Xianglong Liu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the effectiveness of Intra Mix across various GNNs and datasets. |
| Researcher Affiliation | Academia | Shenghe Zheng, Hongzhi Wang , Xianglong Liu Massive Data Computing Lab, Harbin Institute of Technology EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Workflow of Intra Mix |
| Open Source Code | Yes | Our code is available at: https://github.com/Zhengsh123/Intra Mix. |
| Open Datasets | Yes | Datasets: We evaluate Intra Mix on commonly used medium-scale semi-supervised datasets for node classification, including Cora, Cite Seer, Pubmed [34], CS, and Physics [35]. ... We also conduct semi-supervised experiments on large-scale graphs, including ogbn-arxiv [16] and Flickr [49]. |
| Dataset Splits | Yes | We follow the original splits for these datasets. We also conduct semi-supervised experiments on large-scale graphs, including ogbn-arxiv [16] and Flickr [49]. To alter the original splits for full-supervised training on these datasets, we use 1% and 5% of the original training data for semi-supervised experiments, respectively. Details can be found in Appendix C.1. (Section 4.1, Semi-supervised Learning) and Table 8 which provides "Split Ratio" (e.g., "8.5/30.5/61 Accuracy" for Cora). |
| Hardware Specification | Yes | All experiments are conducted on a single NVIDIA RTX-3090. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with version numbers (e.g., Python version, library versions like PyTorch, TensorFlow, or scikit-learn versions). |
| Experiment Setup | Yes | For each graph augmentation applied to each GNN, we use the same hyperparameters for fairness. When comparing with other methods, we use the settings from their open-source code and report the average results over 30 runs. (Section 4.1, Semi-supervised Learning) and "Sensitivity analysis of λ indicates that the best performance is achieved when λ = 0.5." (Figure 3 caption) and "Therefore, we choose λ B(2, 2), where B denotes Beta Distribution." (Section 4.4, Ablation Experiment, Sensitivity Analysis of λ) |