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..
GLASS: GNN with Labeling Tricks for Subgraph Representation Learning
Authors: Xiyuan Wang, Muhan Zhang
ICLR 2022 | Venue PDF | 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 EMAIL |
| 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. |