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 | Conference PDF | Archive PDF | Plain Text | 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. |