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..
Towards More Practical Adversarial Attacks on Graph Neural Networks
Authors: Jiaqi Ma, Shuangrui Ding, Qiaozhu Mei
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the proposed procedure can significantly increase the mis-classification rate of common GNNs on real-world data without access to model parameters nor predictions. |
| Researcher Affiliation | Academia | School of Information, University of Michigan, Ann Arbor, Michigan, USA. Department of EECS, University of Michigan, Ann Arbor, Michigan, USA. |
| Pseudocode | Yes | Algorithm 1: The GC-RWCS Strategy for Node Selection. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We adopt three citation networks, Citeseer, Cora, and Pubmed, which are standard node classification benchmark datasets [26]. |
| Dataset Splits | Yes | Following the setup of JK-Net [25], we randomly split each dataset by 60%, 20%, and 20% for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions using GCN and JK-Net models and following hyper-parameter setups from a prior work [25], but it does not specify any software dependencies with version numbers, such as programming languages, libraries, or frameworks (e.g., Python version, PyTorch/TensorFlow version). |
| Experiment Setup | Yes | We set the number of layers for GCN as 2 and the number of layers for both JK-Concat and JK-Maxpool as 7. The hidden size of each layer is 32. For the proposed GC-RWCS strategy, we fix the number of step L = 4, the neighbor-hop parameter k = 1 and the parameter l = 30 for the binarized f M in Eq. (4) for all models on all datasets. |