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..
Discovering conflicting groups in signed networks
Authors: Ruo-Chun Tzeng, Bruno Ordozgoiti, Aristides Gionis
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental evaluation shows that, compared to state-of-the-art baselines, our methods find solutions of higher quality, are faster, and recover ground-truth conflicting groups with higher accuracy. |
| Researcher Affiliation | Academia | Ruo-Chun Tzeng KTH Royal Institute of Technology EMAIL Bruno Ordozgoiti Aalto University EMAIL Aristides Gionis KTH Royal Institute of Technology EMAIL |
| Pseudocode | Yes | Algorithm 1: SCG (A, k) Spectral Conflicting Group detection; Algorithm 2: Solve-Max-DRQ (A, q) Find maximum discrete Rayleigh quotient; Algorithm 3: Min Angle Round (v, q) Deterministic rounding by minimum-angle heuristic; Algorithm 4: Random Round (v, q) Randomized rounding |
| Open Source Code | Yes | All methods have been implemented in Python 3.1https://github.com/rutzeng/SCG-Neur IPS2020. |
| Open Datasets | Yes | The datasets we have used are all publicly available and the detailed information can be found in Supplementary D.1. (Section 7) |
| Dataset Splits | No | The paper discusses evaluating on synthetic and real-world graphs but does not specify any training, validation, or test dataset splits in terms of percentages, sample counts, or explicit splitting methodologies typically used for model training and evaluation. |
| Hardware Specification | Yes | All the experiments are executed on a machine with Intel Core i5 at 1.8 GHz with 8 GB RAM. |
| Software Dependencies | No | The paper states 'All methods have been implemented in Python 3.' but does not provide specific version numbers for any other software libraries or dependencies used (e.g., NumPy, SciPy, etc.). |
| Experiment Setup | No | The paper details hyperparameters for a baseline method (KOCG) as 'α = 1/(k 1), β = 50, and ℓ= 5000', but it does not provide specific hyperparameter values or detailed training configurations for its own proposed methods (SCG-MA, SCG-MO, SCG-B, SCG-R). |