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..
HGPrompt: Bridging Homogeneous and Heterogeneous Graphs for Few-Shot Prompt Learning
Authors: Xingtong Yu, Yuan Fang, Zemin Liu, Xinming Zhang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we thoroughly evaluate and analyze HGPROMPT through extensive experiments on three public datasets. |
| Researcher Affiliation | Academia | 1University of Science and Technology of China, China 2 Singapore Management University, Singapore 3 National University of Singapore, Singapore |
| Pseudocode | No | No explicit pseudocode or algorithm blocks are provided in the main text, only mathematical formulas and descriptions of the framework components. |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing the code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | We conduct experiments on three benchmark datasets. (1) ACM serves as a citation network... (2) DBLP serves as an all-encompassing bibliographic database... (3) Freebase (Bollacker et al. 2008)... For all these datasets, we employ the same raw data as Simple-HGN (Lv et al. 2021). We provide a summary of these datasets in Table 1. |
| Dataset Splits | Yes | we randomly generate 100 one-shot tasks for model training and validation. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (CPU, GPU models, memory, etc.) used for running the experiments. |
| Software Dependencies | No | We adopt Micro F and Macro F (Pedregosa et al. 2011; Lv et al. 2021) as the evaluation metrics. |
| Experiment Setup | Yes | For hyperparameter settings and other implementation details about the baselines and HGPROMPT, see Appendix C. |