UAG: Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks
Authors: Boyuan Feng, Yuke Wang, Yufei Ding7404-7412
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Intensive experiments show that our proposed defense approach outperforms the state-of-the-art solutions by a significant margin. Rigorous experiments and studies across various datasets on mainstream GNNs show that our proposed defense approach outperforms the state-of-the-art RGCN by a significant margin. |
| Researcher Affiliation | Academia | Boyuan Feng, Yuke Wang, Yufei Ding University of California, Santa Barbara {boyuan, yuke_wang, yufeiding}@ucsb.edu |
| Pseudocode | No | The paper describes the methodology using prose and mathematical equations but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not explicitly state that source code for the described methodology is publicly available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We select the most typical datasets (Cora, Citeseer, and Pubmed) used by many GNN papers (Kipf and Welling 2017; Xu et al. 2019b; Hamilton, Ying, and Leskovec 2017) to evaluate our UAG. |
| Dataset Splits | Yes | We follow the common data split by selecting 10% nodes as the training dataset, 10% nodes as the validation dataset, and 80% nodes as the testing dataset. |
| Hardware Specification | Yes | We evaluate UAG on a Dell Workstation T7910 (Ubuntu 18.04) with an Intel Xeon CPU E5-2603, 64 GB memory, and an NVIDIA 1080Ti GPU with 12GB memory. |
| Software Dependencies | No | We implement UAG based on Py Torch Geometric (Fey and Lenssen 2019). No specific version numbers for PyTorch Geometric or other software dependencies are provided. |
| Experiment Setup | Yes | In our experiments, we can achieve satisfying results by using ατ = 15 and βτ = 2.5. |