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..
Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs
Authors: Jiarong Xu, Yizhou Sun, Xin Jiang, Yanhao Wang, Chunping Wang, Jiangang Lu, Yang Yang4299-4307
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that, even with no exposure to the model, the Macro-F1 drops 5.5% in node classification and 29.5% in graph classification, which is a significant result compared with existent works. |
| Researcher Affiliation | Collaboration | 1Zhejiang University 2University of California, Los Angeles 3Fin Volution Group |
| Pseudocode | No | The paper describes the attack strategy in steps (i), (ii), (iii) in Section 3.3, but it is presented as a descriptive paragraph rather than a formally structured pseudocode block or algorithm. |
| Open Source Code | Yes | Our codes are available at: https: //github.com/galina0217/stack. |
| Open Datasets | Yes | For node-level attacks, we adopt Cora-ML, Citeseer and Polblogs for node classification task, and follow the preprocessing in (Dai et al. 2018). |
| Dataset Splits | Yes | We set the training/validation/test split ratio as 0.1:0.1:0.8 |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or memory used for the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation or experimentation. |
| Experiment Setup | Yes | We set the spatial coefficient k = 1 and the restart threshold τ = 0.03. The candidate set of adversarial edges is randomly sampled in every trial, and its size is set as 20K. |