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 Attack on Graph Structured Data
Authors: Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use both synthetic and real-world data to show that, a family of Graph Neural Network models are vulnerable to these attacks, in both graph-level and node-level classification tasks. We also show such attacks can be used to diagnose the learned classifiers. and 4. Experiment |
| Researcher Affiliation | Collaboration | 1Georgia Institute of Technology 2Ant Financial 3Tsinghua University. |
| Pseudocode | No | The paper describes algorithms using prose and mathematical equations but does not present any formal pseudocode or 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 | Here we use four real-world datasets, namely the Citeseer, Cora, Pubmed and Finance. and Table 3. Statistics of the graphs used for node classification. and We use GCN (Kipf & Welling, 2016) as the target model to attack. |
| Dataset Splits | Yes | The dataset is divided into training and two test sets. The test set I contains 1,500 graphs, while test set II contains 150 graphs. and Table 3. Statistics of the graphs used for node classification. with columns Train/Test I/Test II for real-world datasets, e.g., Citeseer 120/1,000/500. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions models like GNNs, structure2vec, and GCN but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, or TensorFlow versions). |
| Experiment Setup | Yes | For Genetic Alg, we set the population size |P| = 100 and the number of rounds R=10. We tune the crossover rate and mutation rate in {0.1,...,0.5}. For RL-S2V, we tune the number of propagations of its S2V model K ={1,...,5}. and We choose structure2vec as the target model for attack. We also tune its number of propagation parameter K ={2,...,5}. and We use GCN (Kipf & Welling, 2016) as the target model to attack. Here the small modifications is used to regulate the attacker. That is to say, given a graph G and target node c, the adversarial samples are limited to delete single edge within 2-hops of node c. |