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..
Robust Graph Dimensionality Reduction
Authors: Xiaofeng Zhu, Cong Lei, Hao Yu, Yonggang Li, Jiangzhang Gan, Shichao Zhang
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results indicated that our proposed method outperformed all the comparison methods in terms of different classification tasks. |
| Researcher Affiliation | Academia | Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, 541004, China EMAIL, Cong L EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the optimization algorithm in prose and mathematical equations but does not present it in a pseudocode block or algorithm format. |
| Open Source Code | No | The paper does not mention providing open-source code for the methodology described. |
| Open Datasets | No | We downloaded two binary-class datasets and two multi-class benchmark datasets from public website and listed their details in Table 1. (The paper mentions downloading data from a 'public website' and lists dataset names, but does not provide specific URLs, DOIs, repositories, or formal citations with authors/year for dataset access.) |
| Dataset Splits | Yes | During the training process, we used a 5-fold cross validation method to conduct model selection. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like Support Vector Machine (SVM) but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | In model selection, we set parameters of all the comparison methods by following their corresponding literature and set the parameter λ in our method as {10 2, 10 1, . . . , 102}, and selected the parameters combination with the best performance for testing. |