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..
Unlocking the Potential of Black-box Pre-trained GNNs for Graph Few-shot Learning
Authors: Qiannan Zhang, Shichao Pei, Yuan Fang, Xiangliang Zhang
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real-world datasets for few-shot node classification validate the effectiveness of our proposed method in the black-box setting. |
| Researcher Affiliation | Academia | 1Cornell University, USA 2University of Massachusetts Boston, USA 3Singapore Management University, Singapore 4University of Notre Dame, USA EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the proposed model and optimization steps using mathematical equations and textual explanations, but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Implementation can be found at https://github.com/repograph/metabp. |
| Open Datasets | Yes | We leverage four real-world graph datasets for experimental evaluation following previous works (Zhou et al. 2019; Wu et al. 2022), including Cora (Yang, Cohen, and Salakhudinov 2016), Amazon Computers (Zhang et al. 2022b), Cora-full (Bojchevski and G unnemann 2018), and OGBN-arxiv (Hu et al. 2020a). |
| Dataset Splits | Yes | For dataset splitting (train/val/test), we used ratios of 3/2/2 for Cora, 4/3/3 for Computers, 25/20/25 for Cora-Full, and 20/10/10 for OGBN-Arxiv. |
| Hardware Specification | Yes | We implement Meta-BP in Py Torch with an NVIDIA Tesla V100 GPU and use a two-layer DGI of 256 hidden units as the black-box pretrained GNN |
| Software Dependencies | No | The paper mentions 'Py Torch' as a software framework but does not provide a specific version number. No other software dependencies with version numbers are listed. |
| Experiment Setup | Yes | Dimensions of the learnable transformation layer in GML upon node representations are determined via a grid search over {4, 8, 32, 64, 128}. The neural estimator is established as a two-layer MLP with 64 units. β is 1.0 for the information bottleneck regularization and α is 0.1 for meta-optimization. Learning rates of all models are searched from {0.01, 0.005, 0.001, 0.0005, 0.0001}. MAML-based approaches including Meta-BP adopt two fast updates with a step size of 0.05, except that on Amazon Computers it applies 0.01 as the step size. |