Hyperbolic Representation Learning: Revisiting and Advancing
Authors: Menglin Yang, Min Zhou, Rex Ying, Yankai Chen, Irwin King
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments across various models and different tasks demonstrate the versatility and adaptability of the proposed method. Remarkably, our method achieves a remarkable improvement of up to 21.4% compared to the competing baselines. |
| Researcher Affiliation | Collaboration | 1Department of Computer Sciences and Engineering, The Chinese University of Hong Kong 2Huawei Technologies Co., Ltd. 3Yale University. |
| Pseudocode | Yes | Algorithm 1 Hyperbolic Informed Embedding (HIE) |
| Open Source Code | No | The paper does not contain an explicit statement about releasing its source code, nor does it provide a link to a repository. |
| Open Datasets | Yes | we perform evaluations on four public available datasets, namely DISEASE, AIRPORT, CORA, CITESEER and. For more details about these datasets, please refer to Appendix F.1. Table 7. Statistics of the datasets. |
| Dataset Splits | Yes | For the link prediction task, we randomly split the edges in the DISEASE dataset into training (75%), validation (5%), and test (20%) sets for the shallow models. For node classification, we split the nodes in the AIRPORT dataset into 70%, 15%, and 15%, and the nodes in the DISEASE dataset into 30%, 10%, and 60%. |
| Hardware Specification | No | The paper only states 'on the NVIDIA GPUs' without specifying the model or other hardware details. |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'Adam' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | For all models, we traverse the number of embedding dimensions from 8, 64, 256 and then perform a hyper-parameter search on a validation set over learning rate {0.01, 0.02, 0.005}, weight decay {1e 4, 5e 4, 5e 5}, dropout {0.1, 0.2, 0.5, 0.6}, and the number of layers {1, 2, 3, 4, 5}. We also adopt the early stopping strategies based on the validation set with patience in {100, 200, 500}. |