RoboGNN: Robustifying Node Classification under Link Perturbation
Authors: Sheng Guan, Hanchao Ma, Yinghui Wu
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using real-world benchmark graphs, we experimentally verify that Robo GNN can effectively robustify representative GNNs with guaranteed robustness, and desirable gains on accuracy. We next experimentally verify the effectiveness of Robo GNN on improving the robustness and accuracy of GNN-based classification, the learning cost, and the impact of parameters. |
| Researcher Affiliation | Academia | Sheng Guan , Hanchao Ma , Yinghui Wu Case Western Reserve University {sxg967,hxm382,yxw1650}@case.edu |
| Pseudocode | Yes | Algorithm 1 min Protect; Algorithm 2 Robo GNN |
| Open Source Code | Yes | The source code and datasets are available1. 1https://github.com/CWRU-DB-Group/robognn |
| Open Datasets | Yes | We use three real-world datasets: Cora [Mc Callum et al., 2000], Citeseer [Giles et al., 1998] and Pubmed [Sen et al., 2008]. |
| Dataset Splits | Yes | Table 1: Settings: Datasets, training, and robustification... # Training Nodes 140 120 60 # Validation Nodes 500 500 500 # Test Nodes 1,000 1,000 1000 |
| Hardware Specification | Yes | All Experiments are executed on a Unix environment with GPU Nvidia P-100. |
| Software Dependencies | No | The paper mentions software like GCN, GAT, and π-PPNP but does not specify exact version numbers for these or any other ancillary software components (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We train a two-layer network for all the input models with the same set of hyper-parameters settings (e.g., dropout rate, number of hidden units). The training epoch number is set as 300. For each dataset, we fix the learning rate for Pro-GNN, cert PPNP, and Robo GNN. |