Local-Global Defense against Unsupervised Adversarial Attacks on Graphs
Authors: Di Jin, Bingdao Feng, Siqi Guo, Xiaobao Wang, Jianguo Wei, Zhen Wang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our strategies can enhance the robustness of representation against various adversarial attacks on three benchmark graphs. |
| Researcher Affiliation | Academia | Di Jin1, Bingdao Feng1, Siqi Guo1, Xiaobao Wang1*, Jianguo Wei1, Zhen Wang2 1College of Intelligence and Computing, Tianjin University, Tianjin, China 2School of Cybersecurity, Northwestern Polytechnical University, Xi an, Shaanxi, China |
| Pseudocode | Yes | Algorithm 1: Optimization algorithm |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | We use three real-world datasets in our experiments, i.e., Cora, Citeseer (Sen et al. 2008) and Polblogs (Adamic and Glance 2005). |
| Dataset Splits | Yes | For Cora and Citeseer, randomly assign them to the training, verification, and test sets in the ratio of 1:1:8. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper mentions various models and frameworks (e.g., GCN, GAT, DGI) but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | For our model, we set the parameters h = 0.2, α = 1 and β = 0.4. At the stage of evaluating, we consider both the performance and the robustness of the model, so we employ the four attack methods indicated above, and set different perturbation ratios with a step of 10%. All comparative learning baselines use a two-layer GCN as the encoder and use the default setting. |