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..
Adversarial Examples for Graph Data: Deep Insights into Attack and Defense
Authors: Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, Liming Zhu
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on a number of datasets show the effectiveness of the proposed techniques. |
| Researcher Affiliation | Academia | 1University of New South Wales, Australia 2Data61, CSIRO 3National University of Defense Technology, China |
| Pseudocode | Yes | Algorithm 1 shows the pseudo-code for untargeted IGJSMA attack. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | We use the widely used CORA-ML [Mc Callum et al., 2000], CITESEER [Bojchevski and G unnemann, 2018] and Polblogs [Adamic and Glance, 2005] datasets. |
| Dataset Splits | Yes | We split each graph into a labeled (20%) set and an unlabeled set of nodes (80%). Among the labeled nodes, half of them are used for training while the rest are used for validation. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU, CPU model) used for running the experiments. It only mentions 'our non-optimized Python implementation'. |
| Software Dependencies | No | The paper mentions 'Python implementation' but does not provide specific software dependencies with version numbers (e.g., PyTorch 1.x, TensorFlow 2.x). |
| Experiment Setup | No | The paper mentions training a 'two-layer GCN' but does not provide specific hyperparameters such as learning rate, batch size, number of epochs, or optimizer details required for reproduction. |