Multi-class Graph Clustering via Approximated Effective $p$-Resistance
Authors: Shota Saito, Mark Herbster
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we provide experiments comparing our approximated p-resistance clustering to other p-Laplacian based methods. ... Our experiment demonstrates that our algorithm outperforms the existing multi-class clustering using graph p-Laplacian and 2-resistance-based methods. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University College London, London, United Kingdom. |
| Pseudocode | Yes | Algorithm 1 Clustering Algorithm via p-Resistance |
| Open Source Code | Yes | The implementation of our method is available at https://github.com/Shota SAITO/ approximated-presistance. |
| Open Datasets | Yes | Our experiments were performed on classification datasets, ionosphere, iris and wine from UCI repository. We also used Hopkins155 dataset (Tron & Vidal, 2007)... |
| Dataset Splits | No | The paper does not explicitly state specific training, validation, and test dataset splits by percentages, counts, or by referencing predefined splits with citations. It mentions using classification datasets for two-class and multi-class tasks but without specifying how data was partitioned for training and validation. |
| Hardware Specification | Yes | Our experiment was conducted on Mac Studio with M1 Max Processor and 32Gi B RAM. |
| Software Dependencies | No | The paper mentions: "Also, we use an Intel binary Matlab translated by Rosetta, which is a standard use in Mac OS with Apple Silicon environment." While Matlab is named, a specific version number for Matlab itself or any other libraries/solvers used is not provided. |
| Experiment Setup | Yes | We used free parameters µPt0.04, 0.06, 0.08, 0.1, 1u, σPt10 3, . . . , 102u and p Pt1.1, 1.4, . . . , 2.9, 5, 10, 100, 1000u. |