Bootstrapping Informative Graph Augmentation via A Meta Learning Approach
Authors: Hang Gao, Jiangmeng Li, Wenwen Qiang, Lingyu Si, Fuchun Sun, Changwen Zheng
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, the experiments across multiple benchmark datasets demonstrate that MEGA outperforms the state-of-the-art methods in graph self-supervised learning tasks. Further experimental studies prove the effectiveness of different terms of MEGA. |
| Researcher Affiliation | Academia | 1University of Chinese Academy of Sciences 2Institute of Software Chinese Academy of Sciences 3Tsinghua University |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Our codes are available at https://github.com/hang53/MEGA. |
| Open Datasets | Yes | Datasets. We evaluate our method on twelve benchmark datasets in two major categories: 1) Social Networks: RDTM5K, IMDB-B, IMDB-M from TU Dataset [Morris et al., ]. 2) Molecules: PROTEINS, MUTAG, COLLAB and DD from TU Dataset [Morris et al., ] and molesol, mollipo, molbbbp, moltox21 and molsider from Open Graph Benchmark (OGB) [Hu et al., 2020a]. |
| Dataset Splits | Yes | We followed the experimental protocol of AD-GCL, including the train/validation/test splits. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We adopt the Adam optimizer with a learning rate of 10 4 for learnable graph augmentation and a learning rate of 10 3 for graph encoding. We use 50 training epochs on all datasets. |