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..
Robust Graph Neural Networks via Unbiased Aggregation
Authors: Zhichao Hou, Ruiqi Feng, Tyler Derr, Xiaorui Liu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our comprehensive experiments confirm the strong robustness of our proposed model under various scenarios, and the ablation study provides a deep understanding of its advantages. Our code is available at https://github.com/chris-hzc/RUNG. |
| Researcher Affiliation | Academia | Zhichao Hou1 Ruiqi Feng1 Tyler Derr2 Xiaorui Liu1 1North Carolina State University, 2Vanderbilt University |
| Pseudocode | No | The paper describes algorithms using mathematical equations and prose but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | Yes | Our code is available at https://github.com/chris-hzc/RUNG. |
| Open Datasets | Yes | We test our RUNG with the node classification task on two widely used real-world citation networks, Cora ML and Citeseer [29], as well as a large-scale networks Ogbn-Arxiv [30]. |
| Dataset Splits | Yes | We adopt the data split of 10% training, 10% validation, and 80% testing, and report the classification accuracy of the attacked nodes following [5]. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions libraries/frameworks implicitly through citations but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The model hyperparameters including learning rate, weight decay, and dropout rate are tuned as in [5]. Other hyperparameters follow the settings in the original papers. RUNG uses an MLP connected to 10 graph aggregation layers following the decoupled GNN architecture of APPNP. ˆλ = 1 / (1 + λ) is tuned in {0.7, 0.8, 0.9}, and γ tuned in {0.5, 1, 2, 3, 5}. We chose the hyperparameter setting that yields the best robustness without a notable impact (smaller than 1%) on the clean accuracy following [35]. |