Disentangled Contrastive Learning on Graphs
Authors: Haoyang Li, Xin Wang, Ziwei Zhang, Zehuan Yuan, Hang Li, Wenwu Zhu
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of our method against several state-of-the-art baselines. |
| Researcher Affiliation | Collaboration | 1Tsinghua University, 2Bytedance |
| Pseudocode | No | The paper describes the model framework and optimization details in text and diagrams, but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not explicitly provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | To demonstrate the advantages of our method, we conduct experiments on nine wellknown graph classification datasets including four bioinformatics datasets, i.e., MUTAG, PTC-MR, NCI1, PROTEINS, and five social network datasets, i.e., COLLAB, IMDB-BINARY, IMDB-MULTI, REDDIT-BINARY, and REDDIT-MULTI-5K. We also adopt a larger graph dataset ogbg-molhiv from Open Graph Bench Mark (OGB) [26]. |
| Dataset Splits | Yes | We adopt the 10-fold cross validation accuracy, and report the mean accuracy (%) with standard variation after five repeated runs. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'GIN [4] as the message-passing layers' but does not provide specific ancillary software details with version numbers (e.g., Python, PyTorch versions or library versions). |
| Experiment Setup | Yes | For a fair comparison, the hyper-parameters of the graph augmentations are kept consistent with Graph CL. ... Since the ground-truth number of the latent factors is unknown, we search the number of channels K from 1 to 10. |