Universal Prompt Tuning for Graph Neural Networks
Authors: Taoran Fang, Yunchao Zhang, YANG YANG, Chunping Wang, Lei Chen
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results under various pre-training strategies indicate that our method performs better than finetuning, with an average improvement of about 1.4% in full-shot scenarios and about 3.2% in few-shot scenarios. Moreover, our method significantly outperforms existing specialized prompt-based tuning methods when applied to models utilizing the pre-training strategy they specialize in. These numerous advantages position our method as a compelling alternative to fine-tuning for downstream adaptations. Our code is available at: https://github.com/zjunet/GPF. |
| Researcher Affiliation | Collaboration | Taoran Fang1, Yunchao Zhang1, Yang Yang1 , Chunping Wang2, Lei Chen2 1Zhejiang University, 2Fin Volution Group |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at: https://github.com/zjunet/GPF. |
| Open Datasets | Yes | As for the benchmark datasets, we employ the chemistry and biology datasets published by Hu et al. [2020a]. A comprehensive description of these datasets can be found in the appendix. [...] The Chemistry dataset, it comprises 2 million unlabeled molecules sampled from the ZINC15 database [Sterling and Irwin, 2015]. [...] For graph-level multi-task supervised pre-training, a preprocessed Ch EMBL dataset [Mayr et al., 2018, Gaulton et al., 2012] is employed. |
| Dataset Splits | No | The paper mentions limiting training samples for few-shot scenarios and tuning hyperparameters, which typically involves a validation set. However, it does not explicitly provide details about training/validation/test splits, percentages, or sample counts for a specific validation set (e.g., 'X% for validation'). |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU models, or cloud computing instance types used for running experiments. |
| Software Dependencies | No | The paper mentions using a '5-layer GIN' model and provides hyper-parameter settings, but it does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The projection head θ is selected from a range of [1, 2, 3]-layer MLPs with equal widths. The hyper-parameter k of GPF-plus is chosen from the range [5,10,20]. Further details on the hyper-parameter settings can be found in the appendix. [...] Table 11: The hyper-parameter settings. |