Adversarial Attacks on Graph Classifiers via Bayesian Optimisation
Authors: Xingchen Wan, Henry Kenlay, Robin Ru, Arno Blaas, Michael A Osborne, Xiaowen Dong
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 {xwan,kenlay,robin,arno,mosb,xdong}@robots.ox.ac.uk |
| 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... |