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 Attacks on Graph Classifiers via Bayesian Optimisation
Authors: Xingchen Wan, Henry Kenlay, Robin Ru, Arno Blaas, Michael A Osborne, Xiaowen Dong
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate the effectiveness and flexibility of the proposed method on a wide range of graph classification tasks involving varying graph properties, constraints and modes of attack. |
| Researcher Affiliation | Academia | Xingchen Wan Henry Kenlay Binxin Ru Arno Blaas Michael A. Osborne Xiaowen Dong Machine Learning Research Group, University of Oxford, Oxford, UK EMAIL |
| Pseudocode | Yes | The overall routine of our proposed GRABNEL is presented in Fig 1 (and in pseudo-code form in App A) |
| Open Source Code | Yes | An open-source implementation is available at https://github.com/xingchenwan/grabnel. |
| Open Datasets | Yes | We first conduct experiments on four common TU datasets [25], namely (in ascending order of average graph sizes in the dataset) IMDB-M, PROTEINS, COLLAB and REDDIT-MULTI-5K. ... We use a 80-10-10 train-validation-test split (with a fixed random seed 0 for all dataset splits) for all TU datasets, as is standard practice [10, 23]. |
| Dataset Splits | Yes | We use a 80-10-10 train-validation-test split (with a fixed random seed 0 for all dataset splits) for all TU datasets, as is standard practice [10, 23]. |
| Hardware Specification | No | The authors acknowledge the Oxford-Man Institute of Quantitative Finance for providing computing resources but do not specify any particular hardware components like CPU or GPU models. |
| Software Dependencies | No | The paper mentions various models and libraries (e.g., GCN, GIN, Deep Graph Library) but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For GRABNEL, we set the initial acquisition population size to 32 and the evolution population size to 64. The total number of GA iterations is 100. We also use 2 iterations for the WL feature extractor on all graphs, except for the Twitter dataset, where we use 3 iterations... The attack budget r = 0.03 for all experiments and B = 40... |