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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Hypergraphs as Weighted Directed Self-Looped Graphs: Spectral Properties, Clustering, Cheeger Inequality
Authors: Zihao Li, Dongqi Fu, Hengyu Liu, Jingrui He
TMLR 2025 | Venue PDF | LLM Run Details | Input Tokens: 29,524 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,161 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Additionally, we provide extensive experiments to validate our theoretical findings from an empirical perspective. ... 5 Supportive Experiments In this section, we demonstrate the effectiveness of our Hyper Clus-G on real-world data. We first describe the experimental settings and then discuss the experiment results. Details are provided in Appendix C. |
| Researcher Affiliation | Collaboration | Zihao Li EMAIL University of Illinois Urbana-Champaign Dongqi Fu EMAIL Meta Hengyu Liu EMAIL University of Illinois Urbana-Champaign Jingrui He EMAIL University of Illinois Urbana-Champaign |
| Pseudocode | Yes | We name this algorithm as Hyper Clus-G, whose pseudo code is given in Algorithm 1 with complexity analyzed in Appendix B. ... Algorithm 1 Hyper Clus-G |
| Open Source Code | No | The paper does not explicitly provide a link to the source code for the Hyper Clus-G algorithm described in this paper, nor does it state that the code is being released. It only mentions using official code for some baselines. |
| Open Datasets | Yes | We use 9 datasets, all from the UC Irvine Machine Learning Repository. ... Mushroom (https://archive.ics.uci.edu/dataset/73/mushroom) ... Rice (https://archive.ics.uci.edu/dataset/545/rice+cammeo+and+osmancik) ... Car (https://archive.ics.uci.edu/dataset/19/car+evaluation) ... Digit-24 is a subset of Digit (https://archive.ics.uci.edu/dataset/80/optical+recognition+of+ handwritten+digits) ... Covertype (https://archive.ics.uci.edu/dataset/31/covertype) ... Zoo (https://archive.ics.uci.edu/dataset/111/zoo) ... Wine-567 (https://archive.ics.uci.edu/dataset/186/wine+quality) ... Letter (https://archive.ics.uci.edu/dataset/80/optical+recognition+of+handwritten+digits) |
| Dataset Splits | Yes | Among the 9 datasets, 5 of them have 2 classes, and 4 of them have at least 3 classes. For 2-class datasets, we apply Hyper Clus-G to partition the whole EDVW hypergraph into 2 clusters. For k-class datasets (k ≥ 3), we call Hyper Clus-G iteratively for k − 1 times to get a k-way clustering. ... The two clusters have 4208 and 3916 instances, respectively. |
| Hardware Specification | Yes | We run all our experiments on a Windows 11 machine with a 13th Gen Intel(R) Core(TM) i9-13900H CPU, 64GB RAM, and an NVIDIA RTX A4500 GPU. |
| Software Dependencies | No | The Python version in our environment is 3.11.4. In order to run our code, one has to install some other common libraries, including Py Torch, pandas, numpy, scipy, and ucimlrepo. |
| Experiment Setup | No | We did not modify other default hyperparameters in node2vec. ... We tune the training epochs near default so that (1) execution time is acceptable; (2) the NCut value and F1 value of the result are both near convergence. We did not modify other default hyperparameters in event2vec. |