THESAURUS: Contrastive Graph Clustering by Swapping Fused Gromov-Wasserstein Couplings
Authors: Bowen Deng, Tong Wang, Lele Fu, Sheng Huang, Chuan Chen, Tao Zhang
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate the performance of THESAURUS, we run the proposed method on nine attribute graph datasets, including Cora, Citeseer, Pubmed, Amazon-Photo (A-Photo), Cora Full, ACM, DBLP, UAT, and Wiki. The baselines are Kmeans, DEC, GRACE (Zhu et al. 2020), SDCN, DFCN, DCRN, S3GC, SCGC, HSAN, and Dink-Net. Our evaluation protocol follows that of the previous SOTA Dink-Net (Liu et al. 2023a). Besides Normalized Mutual Information (NMI) and Adjusted Rand Index (ARI), the metrics include Accuracy (ACC) and the Macro-F1 score (F1)... |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China 2School of Systems Science and Engineering, Sun Yat-sen University, Guangzhou, China EMAIL, EMAIL |
| Pseudocode | Yes | The illustration of our proposed THESAURUS. And the details are summarized in Algorithm 1 in the appendix. |
| Open Source Code | No | The paper states, 'This research utilizes publicly available datasets and comparison methods, all of which are based on open-source code,' but it does not provide an explicit statement or link indicating that the source code for the proposed THESAURUS method is publicly available. |
| Open Datasets | Yes | To evaluate the performance of THESAURUS, we run the proposed method on nine attribute graph datasets, including Cora, Citeseer, Pubmed, Amazon-Photo (A-Photo), Cora Full, ACM, DBLP, UAT, and Wiki. |
| Dataset Splits | No | The paper states, 'Our evaluation protocol follows that of the previous SOTA Dink-Net (Liu et al. 2023a),' but it does not explicitly provide specific training/test/validation dataset splits or cross-validation details within the main text. |
| Hardware Specification | Yes | Part of the results are summarized in Table 1, with OOM indicating out-of-memory failures on one RTX 4090 GPU. |
| Software Dependencies | No | The paper mentions various algorithms and models such as K-means, GCN, Sinkhorn, and t-SNE, but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as concrete hyperparameter values (e.g., learning rate, batch size, number of epochs, optimizer settings) in the main text. |