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 [1].
Combating Bilateral Edge Noise for Robust Link Prediction
Authors: Zhanke Zhou, Jiangchao Yao, Jiaxu Liu, Xiawei Guo, Quanming Yao, LI He, Liang Wang, Bo Zheng, Bo Han
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | we ο¬rst conduct an empirical study to disclose that the edge noise bilaterally perturbs both input topology and target label, yielding severe performance degradation and representation collapse. ... Extensive experiments on six datasets and three GNNs with diverse noisy scenarios verify the effectiveness of our RGIB instantiations. |
| Researcher Affiliation | Collaboration | 1Hong Kong Baptist University 2CMIC, Shanghai Jiao Tong University 3Shanghai AI Laboratory 4 Alibaba Group 5 Tsinghua Unversity |
| Pseudocode | Yes | Algorithm 1 Hybrid graph augmentation. |
| Open Source Code | Yes | The code is publicly available at: https://github.com/tmlr-group/RGIB. |
| Open Datasets | Yes | Setup. 6 popular datasets and 3 types of GNNs are taken in the experiments. |
| Dataset Splits | Yes | The edge noise is generated based on Def. 3.1 after the commonly used data split where 85% edges are randomly selected for training, 5% as the validation set, and 10% for testing. |
| Hardware Specification | Yes | The software framework is the Pytorch [29], while the hardware is one NVIDIA RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions 'Pytorch' as the software framework but does not specify a version number. |
| Experiment Setup | Yes | Setup. 6 popular datasets and 3 types of GNNs are taken in the experiments. The edge noise is generated based on Def. 3.1 after the commonly used data split where 85% edges are randomly selected for training, 5% as the validation set, and 10% for testing. The AUC is used as the evaluation metric as in [48, 55]. ... A further hyper-parameter study with grid search of the multiple Ξ» in Eq. 3 of RGIB-SSL and Eq. 4 of RGIB-REP are illustrated in Fig. 7. |