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..
Spectral Clustering of Signed Graphs via Matrix Power Means
Authors: Pedro Mercado, Francesco Tudisco, Matthias Hein
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on random graphs and real world datasets confirm the theoretically predicted behaviour of the signed power mean Laplacian and show that it compares favourably with state-of-the-art methods. and 4. Experiments on Wikipedia-Elections |
| Researcher Affiliation | Academia | 1Saarland University 2University of T ubingen 3University of Strathclyde. |
| Pseudocode | Yes | Algorithm 1: Spectral clustering of signed graphs with Lp |
| Open Source Code | No | The paper does not contain an explicit statement about releasing the source code for the methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We now evaluate the Signed Power Mean Laplacian Lp with p { 10, 5, 2, 1, 0, 1} on Wikipedia-Elections dataset (Leskovec & Krevl, 2014). |
| Dataset Splits | No | No explicit training, validation, or test dataset splits (e.g., percentages, counts, or specific predefined split references) are provided in the main text for either the synthetic or real-world datasets. |
| Hardware Specification | No | No specific hardware details (e.g., CPU or GPU models, memory, or cluster specifications) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies, libraries, or frameworks with version numbers (e.g., 'Python 3.8, PyTorch 1.9') are explicitly mentioned in the paper. |
| Experiment Setup | Yes | We set the number of clusters to identify to k = 30 and in Fig. 4 we portray the portion of adjacency matrices of positive and negative edges W + and W corresponding to k 1 clusters sorted according to the corresponding identified clusters. and fixing the sparsity of G+ and G by setting p+ in+ p+ out = 0.1 and p in + p out = 0.1 with two clusters each of size 100. |