Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative
Authors: Tianxin Wei, Yuning You, Tianlong Chen, Yang Shen, Jingrui He, Zhangyang Wang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental findings include: (i) Among fabricated augmentations in Hyper GCL, augmenting hyperedges provides the most numerical gains, implying that higher-order information in structures is usually more downstream-relevant; (ii) Generative augmentations do better in preserving higher-order information to further benefit generalizability; (iii) Hyper GCL also boosts robustness and fairness in hypergraph representation learning. The aforementioned empirical evidences (for generalizability) are drawn from comprehensive experiments on 13 datasets. |
| Researcher Affiliation | Academia | University of Illinois Urbana-Champaign, Texas A&M University, University of Texas at Austin |
| Pseudocode | No | The paper describes the proposed frameworks and methods in text and uses diagrams (e.g., Figure 1, Figure 3) for illustration, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes are released at https://github.com/weitianxin/Hyper GCL. |
| Open Datasets | Yes | We examine our methods on the most comprehensive hypergraph benchmarks with 13 data sets, with statistics shown in Table 1. Please refer to Appendix C for detailed information. We use several widely adopted benchmark datasets, including Cora, Citeseer, Pubmed, Cora-CA, DBLP-CA (from [51]); Zoo, 20News, Mushroom, NTU2012, ModelNet40, Yelp, House, Walmart (from [3]) which are publicly available. So we introduce three newly curated hypergraph data sets: German [48], Recidivism [49] and Credit [50]. |
| Dataset Splits | Yes | By default, we split the data into training/validation/test samples using (10%/10%/80%) splitting percentages. |
| Hardware Specification | No | The provided paper text does not specify explicit hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. While the checklist mentions Appendix C contains this information, the content of Appendix C is not provided in the given text. |
| Software Dependencies | No | The provided paper text does not specify software dependencies with version numbers (e.g., Python version, specific library versions). While the checklist mentions Appendix C has implementation details, the content of Appendix C is not included in the provided text. |
| Experiment Setup | No | The paper states: "All the implementation details are listed in Appendix C. More experiments of the hyperparameters study are given in Appendix A." However, the content of Appendix A and C is not provided in the main paper text, so specific experimental setup details are not directly accessible in the provided text. |