Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Spectral Clustering of Signed Graphs via Matrix Power Means
Authors: Pedro Mercado, Francesco Tudisco, Matthias Hein
ICML 2019 | Venue PDF | LLM Run Details | Input Tokens: 18,599 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,206 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 | 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. |