Provable Training for Graph Contrastive Learning
Authors: Yue Yu, Xiao Wang, Mengmei Zhang, Nian Liu, Chuan Shi
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments on various benchmarks, POT consistently improves the existing GCL approaches, serving as a friendly plugin. |
| Researcher Affiliation | Academia | Yue Yu1, Xiao Wang2 , Mengmei Zhang1, Nian Liu1, Chuan Shi1 1Beijing University of Posts and Telecommunications, China 2 Beihang University, China |
| Pseudocode | Yes | Algorithm 1: Provable Training for GCL |
| Open Source Code | Yes | The complete implementation can be found at https://github.com/Void Haruhi/POT-GCL. We also provide an implementation based on Gamma GL [12] at https://github.com/BUPT-GAMMA/Gamma GL. |
| Open Datasets | Yes | We obtain the datasets from Py G [3]. Although the datasets are available for public use, we cannot find their licenses. The datasets can be found in the URLs below: Cora, Cite Seer, Pub Med: https://github.com/kimiyoung/planetoid/raw/master/data Blog Catalog: https://docs.google.com/uc?export=download&id=178PqGqh67RUYMMP6SoRHDoIBh8ku5FS&confirm=t Flickr: https://docs.google.com/uc?export=download&id=1tZp3EB20fAC27SYWwax66_8uGsuU62X&confirm=t Computers, Photo: https://github.com/shchur/gnn-benchmark/raw/master/data/npz/ Wiki CS: https://github.com/pmernyei/wiki-cs-dataset/raw/master/dataset |
| Dataset Splits | Yes | For datasets with a public split available [28], including Cora, Cite Seer, and Pub Med, we follow the public split; For other datasets with no public split, we generate random splits, where each of the training set and validation set contains 10% nodes of the graph and the rest 80% nodes of the graph is used for testing. |
| Hardware Specification | Yes | OS: Linux 5.4.0-131-generic CPU: Intel(R) Xeon(R) Gold 6348 CPU @ 2.60GHz GPU: Ge Force RTX 3090 |
| Software Dependencies | No | The paper mentions implementing with 'Py Torch' and using 'Py G' for datasets, but does not specify version numbers for these software components. |
| Experiment Setup | Yes | Table 4: Hyperparameters: (p1 e, p2 e) Models Cora Cite Seer Pub Med Flickr Blog Catalog Computers Photo Wiki CS... Table 5: Hyperparameters: (τ, κ) Models Cora Cite Seer Pub Med Flickr Blog Catalog Computers Photo Wiki CS... Table 6: Hyperparameters: (pot_batch, num_epochs) Models Cora Cite Seer Pub Med Flickr Blog Catalog Computers Photo Wiki CS |