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..
Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering
Authors: Erlin Pan, Zhao Kang
ICML 2023 | Venue PDF | 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 <EMAIL>, Zhao Kang <EMAIL>. |
| 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]. |