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..
Unsupervised Episode Generation for Graph Meta-learning
Authors: Jihyeong Jung, Sangwoo Seo, Sungwon Kim, Chanyoung Park
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results demonstrate the effectiveness of our proposed unsupervised episode generation method for graph meta-learning towards the FSNC task. Our code is available at: https: //github.com/Jhng Jng/Na Q-Py Torch. |
| Researcher Affiliation | Academia | Jihyeong Jung 1 Sangwoo Seo 1 Sungwon Kim 2 Chanyoung Park 1 2 1Department of Industrial & Systems Engineering, KAIST 2Graduate School of Data Science, KAIST. Correspondence to: Chanyoung Park <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Training Meta-learner Meta( ; θ) |
| Open Source Code | Yes | Our code is available at: https: //github.com/Jhng Jng/Na Q-Py Torch. |
| Open Datasets | Yes | We use five benchmark datasets that are widely used in FSNC to comprehensively evaluate the performance of our unsupervised episode generation method: 1) Two product networks (Amazon-Clothing, Amazon-Electronics (Mc Auley et al., 2015)), 2) three citation networks (Cora-Full (Bojchevski & G unnemann, 2018), DBLP (Tang et al., 2008)) in addition to a large-scale dataset ogbn-arxiv (Hu et al., 2020). |
| Dataset Splits | Yes | For Amazon Clothing, as the validation set contains 17 classes, evaluations on 20-way cannot be conducted. Instead, the evaluation is done in 5/10-way 1/5-shot settings, i.e., four settings in total. In the validation and testing phases, we sampled 50 validation tasks and 500 testing tasks for all settings with 8 queries each. |
| Hardware Specification | Yes | OOM: Out Of Memory on NVIDIA RTX A6000 |
| Software Dependencies | No | The paper mentions software components like 'Adam (Kingma & Ba, 2015) optimizer' and '2-layer GCN (Kipf & Welling, 2017)' but does not provide specific version numbers for these or other libraries/frameworks used for implementation. |
| Experiment Setup | Yes | For each dataset except for Amazon-Clothing, we evaluate the performance of the models in 5/10/20-way, 1/5-shot settings, i.e., six settings in total. [...] In the validation and testing phases, we sampled 50 validation tasks and 500 testing tasks for all settings with 8 queries each. and Table 8. Tuned hyperparameters and their range by baselines MAML-like (MAML, G-Meta) Inner step learning rate {0.01, 0.05, 0.1, 0.3, 0.5}, # of inner updates {1, 2, 5, 10, 20}, Meta-learning rate {0.001, 0.003} Proto Net-like (Proto Net, TENT) Learning rate {5e-5, 1e-4, 3e-4, 5e-4, 1e-3, 3e-3, 5e-3} |