GLASS: GNN with Labeling Tricks for Subgraph Representation Learning
Authors: Xiyuan Wang, Muhan Zhang
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on eight benchmark datasets show that GLASS outperforms the strongest baseline by 14.8% on average. And ablation analysis shows that our max-zero-one labeling trick can boost the performance of a plain GNN by up to 105% in maximum, which illustrates the effectiveness of labeling trick on subgraph tasks. Furthermore, training a GLASS model only takes 37% time needed for a Sub GNN on average. |
| Researcher Affiliation | Academia | 1Institute for Artificial Intelligence, Peking University 2Beijing Institute for General Artificial Intelligence {wangxiyuan,muhan}@pku.edu.cn |
| 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/Xi-yuan Wang/GLASS. |
| Open Datasets | Yes | We use four synthetic datasets: density, cut ratio, coreness, component, and four real-world subgraph datasets, namely ppi-bp, em-user, hpo-metab, hpo-neuro. The four synthetic datasets are introduced by Alsentzer et al. (2020)... The four real-world datasets are also provided by Alsentzer et al. (2020). |
| Dataset Splits | Yes | As for dataset division, the real-world datasets take an 80:10:10 split, and the synthetic datasets follow a 50:25:25 split, following (Alsentzer et al., 2020). |
| Hardware Specification | Yes | Models were trained on an Nvidia V100 GPU to measure the train time and were tested on an Nvidia A40 GPU on a Linux server. |
| Software Dependencies | No | The paper mentions using 'Pytorch Geometric and Pytorch' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | Fixed hyperparameters were batch size = 131072, learning rate = 1e 3, hidden dimension = 64. Dropout is selected from [0.0, 0.5] and l ranges from 1 to 5. ... For GLASS, we select the learning rate from {1e 4, 2e 4, 5e 4, 1e 3, 2e 3, 5e 3}; number of layers from {1, 2}; hidden dimension, 64 for real-world datasets and {1, 5, 9, 13, 17}; dropout, 0.5 for real-world datasets and {0.1, 0.2, 0.3} for synthetic datasets; aggregation, {mean, sum, gcn}; pool, {mean, sum, max, size}; batch size, {ns/80, ns/40, ns/20, ns/10}, where ns is the size of datasets. |