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..
Deep Contrastive Graph Learning with Clustering-Oriented Guidance
Authors: Mulin Chen, Bocheng Wang, Xuelong Li
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on several benchmark datasets demonstrate the superiority of DCGL against stateof-the-art algorithms. |
| Researcher Affiliation | Academia | Mulin Chen1,2, Bocheng Wang1,2, Xuelong Li1,2* 1 School of Artificial Intelligence, OPtics and Electro Nics (i OPEN), Northwestern Polytechnical University, Xi an 710072, Shanxi, China 2 Key Laboratory of Intelligent Interaction and Applications, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi an 710072, Shanxi, China |
| Pseudocode | Yes | Algorithm 1: DCGL |
| Open Source Code | No | The paper does not provide a statement or link indicating the availability of its own source code. |
| Open Datasets | Yes | Seven publicly available datasets are collected as benchmarks, including regular record types TOX-171 and Isolet, image types ORL, Yale B, PIE and USPS, and text type TR41. |
| Dataset Splits | No | The paper lists the total number of samples for each dataset but does not explicitly provide specific train/validation/test dataset splits or their percentages. |
| 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 mentions optimizers and clustering algorithms but does not provide specific software names with version numbers. |
| Experiment Setup | Yes | For DCGL, the hyper-parameters α, β, and γ are fixed to 1, 10^3, and 2 × 10^3 respectively. The neighbor number for LPG rises every 6 epochs. The maximum epoch number is 30. To ensure objectivity, the random seed is fixed before code execution, and each algorithm is repeated 10 times. |