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 [1].
Community-Centric Graph Convolutional Network for Unsupervised Community Detection
Authors: Dongxiao He, Yue Song, Di Jin, Zhiyong Feng, Binbin Zhang, Zhizhi Yu, Weixiong Zhang
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on real networks showed that our new method outperformed the best existing methods, showing the effectiveness of the novel decoding mechanism for generating links and attributes together over the commonly used methods for reconstructing links alone. |
| Researcher Affiliation | Academia | 1College of Intelligence and Computing, Tianjin University, Tianjin 300350, China 2Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA |
| Pseudocode | No | The paper describes the proposed method mathematically and textually but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | We used nine public datasets with known communities (Table 1). Datasets: Texas, Cornell, Washington, Wisconsin, Twitter, Cora, Citeseer, UAI2010, Pubmed. |
| Dataset Splits | No | The paper mentions using benchmark datasets but does not provide specific training, validation, or test dataset splits or a methodology for generating them. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions using 'Tensor Flow' but does not provide specific version numbers for TensorFlow or any other software dependencies. |
| Experiment Setup | No | The paper mentions using the 'Adam optimizer' for training but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or other detailed experimental setup configurations. |