Cluster Attack: Query-based Adversarial Attacks on Graph with Graph-Dependent Priors
Authors: Zhengyi Wang, Zhongkai Hao, Ziqiao Wang, Hang Su, Jun Zhu
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Theoretical analysis and extensive experiments demonstrate the effectiveness of our method by fooling the node classifiers with only a small number of queries. 5 Experiments, 5.1 Experimental Setup, 5.2 Quantitative Evaluation, 5.3 Ablation Study. |
| Researcher Affiliation | Collaboration | Zhengyi Wang1,3 , Zhongkai Hao1 , Ziqiao Wang1 , Hang Su 1,2,3 and Jun Zhu 1,2,3 1Department of Computer Science & Technology, Institute for AI, BNRist Center Tsinghua-Bosch Joint ML Center, THBI Lab, Tsinghua University 2Peng Cheng Laboratory 3 Tsinghua University-China Mobile Communications Group Co., Ltd. Joint Institute |
| Pseudocode | No | More details of our algorithm are deferred to the appendix. |
| Open Source Code | No | The paper does not provide any statements about releasing code, nor does it include links to a code repository. |
| Open Datasets | Yes | Dataset. We do our experiments on Cora and Citeseer [Sen et al., 2008], which are two benchmark small citation networks with discrete node features, and on Reddit [Hamilton et al., 2017] and ogbn-arxiv [Hu et al., 2020], which are two large networks with continuous node features. |
| Dataset Splits | No | The paper does not explicitly provide specific percentages or counts for training, validation, and test dataset splits. It mentions using 'labeled nodes' and 'remaining unlabeled nodes' for prediction, which is common in node classification, but lacks explicit split details. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory used for running the experiments. It only vaguely mentions the 'High Performance Computing Center, Tsinghua University' in the acknowledgements, which is not a specific hardware specification. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers (e.g., Python, PyTorch, TensorFlow versions) needed to replicate the experiment. |
| Experiment Setup | Yes | Without specification, we compare our method with baselines with a trade-off parameter set as λ = 0 in Eq. (5). We uniformly set Nfake = 4 and let the number of victim nodes vary... The number of queries K is set to K = |ΦA| Kt + Nfake Kf, where Kt = Kf = D (feature dimension). For Reddit (with 1500|ΦA| + 750Nfake queries) and obgn-arxiv (with 4000|ΦA| + 2000Nfake queries). |