Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering
Authors: Erlin Pan, Zhao Kang
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on 11 benchmark graphs demonstrate our promising performance. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China. Correspondence to: Erlin Pan <wujisixsix6@gmail.com>, Zhao Kang <zkang@uestc.edu.cn>. |
| Pseudocode | No | The paper describes mathematical formulations and procedures but does not include a clearly labeled 'Algorithm' or 'Pseudocode' block. |
| Open Source Code | Yes | The code is available at DGCN. |
| Open Datasets | Yes | To evaluate the effectiveness of the proposed method, we conduct extensive experiments on 11 benchmarks, including homophilic graph datasets, like Cora, Citeseer (Kipf & Welling), ACM (Fan et al., 2020), AMAP (Liu et al., 2022b), EAT (Mrabah et al., 2022); heterophilic graph datasets, like Texas, Cornell, Wisconsin, Washington (Pei et al., 2020), Twitch (Lim et al., 2021b), and Squirel (Rozemberczki et al., 2021). |
| Dataset Splits | No | The paper does not specify exact percentages, sample counts, or predefined citations for train/validation/test dataset splits needed for reproduction. It focuses on unsupervised clustering metrics. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of 'Adam optimizer' but does not specify any software libraries or their version numbers (e.g., Python, PyTorch, TensorFlow, scikit-learn versions). |
| Experiment Setup | Yes | Our network is trained with Adam optimizer for 500 epochs until convergence. The learning rate of optimizer is set to 1e-2. We tune filter order k in [1, 2, 3, 4, 5, 10]. |