Deep Contrastive Graph Learning with Clustering-Oriented Guidance
Authors: Mulin Chen, Bocheng Wang, Xuelong Li
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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. |