Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiation
Authors: Yaowen Hu, Wenxuan Tu, Yue Liu, Xinhang Wan, Junyi Yan, Taichun Zhou, Xinwang Liu
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on six widely used graph datasets demonstrate that CMV-ND significantly improves the performance of various methods." and "We validate CMV-ND through experiments on six widely used graph datasets, evaluating its superiority, sensitivity, efficiency, robustness, and effectiveness. |
| Researcher Affiliation | Academia | 1College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China 2School of Computer Science and Technology, Hainan University, Haikou 570228, China. Correspondence to: Xinwang Liu <EMAIL>. |
| Pseudocode | Yes | As shown in Algorithm 1, the full procedure of CMV-ND is presented." and "We provide the Py Torch-style pseudocode for our CMV-ND in Algorithm 2. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | Cora: https://docs.dgl.ai/#Cora Graph Dataset Cite Seer: https://docs.dgl.ai/#dgl.data.Citeseer Graph Dataset Amazon-Photo: https://docs.dgl.ai/#dgl.data.Amazon Co Buy Photo Dataset ogbn-arxiv: https://ogb.stanford.edu/docs/nodeprop/#ogbn-arxiv Reddit: https://docs.dgl.ai/#dgl.data.Reddit Dataset ogbn-products: https://ogb.stanford.edu/docs/nodeprop/#ogbn-products |
| Dataset Splits | No | The paper mentions evaluating clustering performance on six graph datasets with a 0.6 missing rate and setting the number of clusters to the ground-truth number of classes, but does not provide specific training/test/validation dataset splits for nodes or graph structure. |
| Hardware Specification | Yes | All experiments are performed on a system equipped with a 24GB RTX 3090 GPU and 64GB RAM. |
| Software Dependencies | Yes | All experiments are implemented using Python 3.9 and Py Torch 1.12. |
| Experiment Setup | Yes | Unless otherwise specified, we set the number of propagation hops to K = 7 and the missing attribute rate to 0.6. For fair comparison, all downstream clustering methods follow the default hyperparameter configurations used in their original implementations. The number of clusters is set to the ground-truth number of classes for each dataset. |