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 | Conference PDF | Archive PDF | Plain Text | 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.