## On Generalized Degree Fairness in Graph Neural Networks
Zemin Liu, Trung-Kien Nguyen, Yuan Fang
Keywords: DMKM: Graph Mining, Social Network Analysis & Community Mining, ML: Bias and Fairness, ML: Graph-based Machine Learning
AAAI/2023/Proceedings/25574 - On Generalized Degree Fairness in Graph Neural Networks.pdf

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[10]

A link to the code is no provided in the paper. Therefore, it is not possible to access the code in a straight way. 
In addition, details about hyperparametes of models and experiments are in the appendix (as mentioned in the paper), but the appendix is not available.

### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[2]

(3/3)

They describe the datasets and the split in their paper.

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[10]

Details about hyperparameters of models and experiments are in the appendix (as mentioned in the paper), but the appendix is not available.

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[1]

The evaluation metrics are well explained. The evaluation procedure and the data aggregation methods are also explained. The data split is explained in detail in the paper. The methodology of the experiments is explained well, so this part is not a problem.

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[5]

The authors included a brief preliminary information on GNNs, but it is not enough if the reader is not familiar with GNNs. The methodology of the experiments is explained well, so this part is not a problem.