Robust Node Classification on Graph Data with Graph and Label Noise
Authors: Yonghua Zhu, Lei Feng, Zhenyun Deng, Yang Chen, Robert Amor, Michael Witbrock
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, We numerically validate the superiority of our method in terms of robust node classification compared with all comparison methods. |
| Researcher Affiliation | Academia | NAOInstitute, University of Auckland, NZ School of Computer Science, University of Auckland, NZ School of Computer Science and Engineering, Nanyang Technological University, Singapore Department of Computer Science, University of Cambridge, UK |
| Pseudocode | Yes | Algorithm 1: The pseudo-code of our RNCGLN method. |
| Open Source Code | Yes | Our code and comprehensive theoretical version are available at: https://github.com/yhzhu66/RNCGLN |
| Open Datasets | Yes | We evaluate the robustness of our proposed method1 on four popular datasets, including three citation datasets (i.e., Cora, Citedeer, Pubmed) (Sen et al. 2008) and one amazon sale dataset (i.e., Photo) (Shchur et al. 2018). |
| Dataset Splits | No | The paper uses well-known datasets but does not explicitly state the training, validation, and test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions software components like MLP and activation functions but does not provide specific version numbers for any software dependencies (e.g., Python version, library versions like PyTorch or TensorFlow). |
| Experiment Setup | No | The paper mentions the existence of several hyperparameters such as α, τ, τg1, τg2, τp1, and τp2, and discusses the warm-up period in terms of epochs. However, it does not provide concrete values for common experimental setup parameters like learning rate, batch size, number of epochs, or optimizer settings. |