Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Adversarial Weight Perturbation Improves Generalization in Graph Neural Networks
Authors: Yihan Wu, Aleksandar Bojchevski, Heng Huang
AAAI 2023 | Venue PDF | 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. |