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..
Understanding Structural Vulnerability in Graph Convolutional Networks
Authors: Liang Chen, Jintang Li, Qibiao Peng, Yang Liu, Zibin Zheng, Carl Yang
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four real-world datasets demonstrate that such a simple but effective method achieves the best robustness performance compared to state-of-the-art models. |
| Researcher Affiliation | Academia | 1Sun Yat-sen University 2Emory University |
| Pseudocode | No | The paper provides mathematical equations for the models but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The codes are available at https://github.com/EdisonLeeeee/Median-GCN. |
| Open Datasets | Yes | The experiments are conducted on four benchmark datasets, including Cora-ML [Mc Callum et al., 2000], Cora, Citeseer and Pubmed [Sen et al., 2008] datasets. |
| Dataset Splits | Yes | The datasets are randomly split into training (10%), validation (10%), and testing (80%) set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using the Adam algorithm but does not specify software dependencies like libraries or frameworks with their version numbers (e.g., 'PyTorch 1.9', 'Python 3.8'). |
| Experiment Setup | Yes | The datasets are randomly split into training (10%), validation (10%), and testing (80%) set. For all models, the number of layers is set to 2 as suggested by previous works [Kipf and Welling, 2017; Velickovic et al., 2018] and the number of hidden units is 64. We employ Adam algorithm [Kingma and Ba, 2015] with an initial learning rate 0.01 to optimize all models. The number of training iterations is 200 with early stopping on the validation set. Following the setting of the work [Wu et al., 2019], the threshold of similarity for removing dissimilar edges is set to 0. The trimmed percentage α of our TMean is set to 0.45 to balance the accuracy and robustness. |