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..
Score Aggregation via Spectral Method
Authors: Mingyu Xiao, Yuqing Wang
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here we give computational results on some real word data as examples. |
| Researcher Affiliation | Academia | Mingyu Xiao and Yuqing Wang School of Computer Science and Engineering, University of Electronic Science and Technology of China, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm SPECTRAL Input: A score matrix X Output: A vector corresponding to the aggregate scores of candidates 1. Compute the judgement matrix Z = XT X; 2. Conduct eigenvalue decomposition to Z, let λmax be the largest eigenvalue and e be the eigenvector corresponding to it; 3. Return v = X e. |
| Open Source Code | No | The paper does not provide any links or explicit statements regarding the availability of open-source code for the described methodology. |
| Open Datasets | Yes | Our data is from Group Lens Research (https://grouplens.org/datasets/movielens/), which collects the rating data for movies from the web site Movie Lens (http://movielens.org) [Harper and Konstan, 2016]. Our data is from the web site of CWTS Leiden Ranking (http://www.leidenranking.com/). |
| Dataset Splits | No | The paper describes using specific datasets but does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the computational examples and applications but does not provide specific experimental setup details such as hyperparameters, training configurations, or system-level settings. |