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..
Hard Sample Aware Network for Contrastive Deep Graph Clustering
Authors: Yue Liu, Xihong Yang, Sihang Zhou, Xinwang Liu, Zhen Wang, Ke Liang, Wenxuan Tu, Liang Li, Jingcan Duan, Cancan Chen
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments and analyses demonstrate the superiority and effectiveness of our proposed method. |
| Researcher Affiliation | Academia | 1College of Computer, National University of Defense Technology 2College of Intelligence Science and Technology, National University of Defense Technology 3Northwestern Polytechnical University 4Beijing Information Science and Technology University |
| Pseudocode | Yes | Algorithm 1: Hard Sample Aware Network |
| Open Source Code | Yes | The source code of HSAN is shared at https://github.com/yueliu1999/HSAN |
| Open Datasets | Yes | To evaluate the effectiveness of our proposed HSAN, we conduct experiments on six benchmark datasets, including CORA, CITE, Amazon Photo (AMAP), Brazil Air Traffic (BAT), Europe Air-Traffic (EAT), and USA Air Traffic (UAT). |
| Dataset Splits | No | The paper does not explicitly describe specific training, validation, and test dataset splits, such as percentages or sample counts for the input data. |
| Hardware Specification | Yes | All experimental results are obtained from the desktop computer with the Intel Core i7-7820x CPU, one NVIDIA Ge Force RTX 2080Ti GPU, 64GB RAM, and the Py Torch deep learning platform. |
| Software Dependencies | No | The paper mentions 'Py Torch deep learning platform' but does not specify a version number or other software dependencies with version numbers. |
| Experiment Setup | Yes | The training epoch number is set to 400...both the attribute encoders and structure encoders are two parameters un-shared one-layer MLPs with 500 hidden units for UAT/AMAP and 1500 hidden units for other datasets. The learnable trade-off α is set to 0.99999 as initialization and reduces to around 0.4 in our experiments. |