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..
Transformers Generalize DeepSets and Can be Extended to Graphs & Hypergraphs
Authors: Jinwoo Kim, Saeyoon Oh, Seunghoon Hong
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we demonstrate the capability of higher-order Transformers on a variety of tasks including synthetic data, large-scale graph regression, and set-to-(hyper)graph prediction. Specifically, we use a synthetic node classification dataset from Gu et. al. (2020) [11], a molecular graph regression dataset from Hu et. al. (2021) [17], two set-to-graph prediction datasets from Serviansky et. al. (2020) [29], and three hyperedge prediction datasets used in Zhang et. al. (2020) [36]. |
| Researcher Affiliation | Academia | Jinwoo Kim, Saeyoon Oh, Seunghoon Hong School of Computing, KAIST EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Our implementation is available at https://github.com/jw9730/hot. |
| Open Datasets | Yes | Specifically, we use a synthetic node classification dataset from Gu et. al. (2020) [11], a molecular graph regression dataset from Hu et. al. (2021) [17], two set-to-graph prediction datasets from Serviansky et. al. (2020) [29], and three hyperedge prediction datasets used in Zhang et. al. (2020) [36]. |
| Dataset Splits | Yes | We used training and test sets with chains of length 20 and 200 respectively. ... As test data is unavailable, we report the Mean Absolute Error (MAE) on validation dataset. |
| Hardware Specification | Yes | Plots are shown until each model runs into out-of-memory error on a RTX 6000 GPU with 22GB. |
| Software Dependencies | No | The paper does not explicitly provide a list of software dependencies with specific version numbers (e.g., 'Python 3.8, PyTorch 1.9, and CUDA 11.1'). |
| Experiment Setup | Yes | Details including the datasets and hyperparameters can be found in Appendix A.2. |