Adversarial Weight Perturbation Improves Generalization in Graph Neural Networks
Authors: Yihan Wu, Aleksandar Bojchevski, Heng Huang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive experiments to show the effect of WT-AWP on the natural and robustness performance of different GNNs for both node classification and graph classification tasks. |
| Researcher Affiliation | Academia | 1 Electrical and Computer Engineering, University of Pittsburgh, PA, USA 2 CISPA Helmholtz Center for Information Security |
| Pseudocode | Yes | Algorithm 1: WT-AWP: Weighted Truncated Adversarial Weight Perturbation |
| Open Source Code | No | No explicit statement or link is provided for the authors' own open-source code. |
| Open Datasets | Yes | Datasets. We use three benchmark datasets, including two citation networks, Cora and Citeseer (Sen et al. 2008), and one blog dataset Polblogs (Adamic and Glance 2005). |
| Dataset Splits | Yes | We use 10% nodes for training, 10% for validating and the rest 80% for testing. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) are provided for running experiments. |
| Software Dependencies | No | The paper mentions using "Pytorch Geometric (Fey and Lenssen 2019) and Deep-Robust (Li et al. 2020)" but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | To achieve fair comparison we keep the same training settings for all models. We use a 2-layer structure... For GCN and PPNP, the hidden dimensionality is 64; for GAT, we use 8 heads with size 8. We choose K = 10, α = 0.1 in PPNP. |