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..
Hypergraph p-Laplacian: A Differential Geometry View
Authors: Shota Saito, Danilo Mandic, Hideyuki Suzuki
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed p-Laplacian is shown to outperform standard hypergraph Laplacians in the experiment on a hypergraph semisupervised learning and normalized cut setting. Our experiment on hypergraph semi-supervised clustering problem shows that our hypergraph p-Laplacian outperforms the current hypergraph Laplacians. |
| Researcher Affiliation | Academia | Shota Saito The University of Tokyo EMAIL Danilo P Mandic Imperial College London EMAIL Hideyuki Suzuki Osaka University EMAIL |
| Pseudocode | No | The paper describes algorithms and update rules using mathematical equations and textual descriptions, but it does not provide structured pseudocode blocks or algorithm figures. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of its source code. |
| Open Datasets | Yes | We summarize the benchmark datasets we used in Table 1. All datasets were taken from UCI Machine Learning Repository. |
| Dataset Splits | Yes | The parameter μ was chosen for all methods from 10k, where k {0, 1, 2, 3, 4} by 5-fold cross validation. We randomly picked up a certain number of labels as known labels, and predicted the remaining ones. We repeated this procedure 10 times for different number of known labels. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as CPU or GPU models. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | The parameter μ was chosen for all methods from 10k, where k {0, 1, 2, 3, 4} by 5-fold cross validation. For our p-Laplacian, we varied p from 1 to 3 with the interval of 0.1, and we show the result of p=2 and the result of p giving the smallest average error for each number of known labeled points. The parameter p for Hein s regularizer is fixed at 2, since this is recommended by Hein et al. (2013). We take ψ(0) = y as an initial condition. |