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 | Archive PDF | Plain Text | 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. |