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..
Attributed Graph Clustering with Dual Redundancy Reduction
Authors: Lei Gong, Sihang Zhou, Wenxuan Tu, Xinwang Liu
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments have demonstrated that AGC-DRR outperforms the state-of-the-art clustering methods on most of our benchmarks. The corresponding code is available at https://github.com/gongleii/AGC-DRR. |
| Researcher Affiliation | Academia | Lei Gong , Sihang Zhou , Wenxuan Tu and Xinwang Liu National University of Defense Technology, Changsha, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 The training procedure of AGC-DRR Input: Graph data {A, X}; Number of clusters K; Maximum iterations T; Hyper-parameter λ Output: Clustering results 1: for t = 1 : T do 2: Calculate W and A to obtain the structure augmented graph by Eq. (9) and Eq. (10), respectively; / Fix N2 and optimize N1 / 3: Calculate C1 and C2 by Eq. (4); 4: Update N1 by minimizing the objective in Eq. (11). / Fix N1 and optimize N2 / 5: Calculate Z1 and Z2 by Eq. (1); 6: Calculate C1 and C2 by Eq. (4); 7: Update N2 by maximizing the objective in Eq. (12). 8: end for 9: Obtain clustering results over the average of C1 and C2 10: return Clustering results |
| Open Source Code | Yes | The corresponding code is available at https://github.com/gongleii/AGC-DRR. |
| Open Datasets | Yes | We evaluate the proposed AGC-DRR on four public benchmark datasets including ACM1, DBLP2, CITE3, and AMAP [Shchur et al., 2018]. 1https://dl.acm.org/ 2https://dblp.uni-trier.de 3http://citeseerx.ist.psu.edu/index |
| Dataset Splits | No | The paper does not explicitly state the training, validation, and test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | Yes | We conduct experiments to evaluate the proposed AGC-DRR on the Py Torch platform with the NVIDIA Ge Force RTX 3080. |
| Software Dependencies | No | We conduct experiments to evaluate the proposed AGC-DRR on the Py Torch platform with the NVIDIA Ge Force RTX 3080. |
| Experiment Setup | Yes | We train AGC-DRR on all benchmark datasets for at least 100 iterations until convergence. ... For our proposed AGC-DRR, we optimize it with the Adam optimizer, the learning rates for N1 and N2 are set to 1e-3 and 1e-4 on CITE, and 1e-4, 5e-4 on others, respectively. The regularized hyper-parameter λ is set as 1 for all datasets. |