Attributed Graph Clustering with Dual Redundancy Reduction
Authors: Lei Gong, Sihang Zhou, Wenxuan Tu, Xinwang Liu
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | 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 glnudt@163.com, xinwangliu@nudt.edu.cn |
| 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. |