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].
Uncertainty in Graph Neural Networks: A Survey
Authors: Fangxin Wang, Yuqing Liu, Kay Liu, Yibo Wang, Sourav Medya, Philip S. Yu
TMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This survey aims to provide a comprehensive overview of the GNNs from the perspective of uncertainty with an emphasis on its integration in graph learning. We compare and summarize existing graph uncertainty theory and methods, alongside the corresponding downstream tasks. |
| Researcher Affiliation | Academia | Fangxin Wang, Yuqing Liu, Kay Liu, Yibo Wang, Sourav Medya, Philip S. Yu EMAIL Department of Computer Science, University of Illinois Chicago |
| Pseudocode | No | The paper is a survey and does not present any structured pseudocode or algorithm blocks for its own methodology. |
| Open Source Code | No | The paper is a survey and does not provide concrete access to source code for its own methodology. It reviews existing literature rather than presenting new implementable methods with associated code. |
| Open Datasets | No | The paper is a survey that discusses various datasets used in the context of the reviewed research (e.g., for molecular property prediction or traffic forecasting), but it does not provide concrete access information for a dataset used in its own methodology. The paper primarily summarizes existing literature. |
| Dataset Splits | No | The paper is a survey and does not conduct original experiments requiring dataset splits. It discusses datasets and methods from other research papers. |
| Hardware Specification | No | The paper is a survey and does not describe any specific hardware used for running its own experiments. |
| Software Dependencies | No | The paper is a survey and does not specify any ancillary software with version numbers used for its own methodology or experiments. |
| Experiment Setup | No | The paper is a survey and does not describe an experimental setup with specific hyperparameters or training configurations for its own methodology. |