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..
Fine-Tuning Graph Neural Networks by Preserving Graph Generative Patterns
Authors: Yifei Sun, Qi Zhu, Yang Yang, Chunping Wang, Tianyu Fan, Jiajun Zhu, Lei Chen
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Compared with existing algorithms, G-TUNING demonstrates consistent performance improvement in 7 in-domain and 7 out-of-domain transfer learning experiments.5 Experiments In this section, we answer the following questions: Q1. (Effectiveness) Does G-TUNING improve the performance of fine-tuning? Q2. (Transferability) Can G-TUNING enable the better transferability than baselines? Q3. (Integrity) How does each component of G-TUNING contribute to the performance? Q4. (Efficiency) Can G-TUNING improve the performance of fine-tuning at an acceptable time consumption? |
| Researcher Affiliation | Collaboration | 1Zhejiang University, Hangzhou, China 2University of Illinois Urbana-Champaign, USA 3Fin Volution Group, Shanghai, China |
| Pseudocode | Yes | The overall learning process of G-TUNING can be found in Algorithm 1 from App A.3. |
| Open Source Code | Yes | 1Supplement materials: https://github.com/zjunet/G-Tuning |
| Open Datasets | Yes | we pre-train GIN (Xu et al. 2019) by self-supervised Context Prediction task on the ZINC15 dataset with 2 million unlabeled molecules (Sterling and Irwin 2015). Next, we perform fine-tuning of the backbone model on 7 binary classification datasets obtained from Molecule Net (Wu et al. 2018).We pre-train on 7 different datasets ranging from academia to social domains, and evaluate our approach on 7 downstream graph classification benchmarks: IMDB-M, IMDB-B, MUTAG, PROTEINS, ENZYMES, MSRC_21 and RDT-M12K from the TUDataset (Morris et al. 2020). |
| Dataset Splits | Yes | We use the scaffold split at an 8:1:1 ratio.We report the results under 10-fold cross-validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. It only mentions general running time without hardware context. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers (e.g., Python 3.8, PyTorch 1.9, etc.). |
| Experiment Setup | Yes | In G-TUNING, we have two major hyper-parameters: the number of learnable bases C and graphon size M.Fig 4 shows that the performance increases as the number of bases grows from 2 to 32. |