
## 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]

No implementation source is given in the paper. No details on their implementation is noted. The authors provide a schematic of their framework. This means the independent investigators would have to start from almost nothing to re-implement the method.

### 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)

The authors use three data sets and provide some details and citations on them, and they seem to be open access. Statistics are provided in table 1 and the authors refer for more details to the appendix which AAAI does not include/allow.

### 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?_

[8]

The is a short note on hyperparameter K's value and the analysis refers to the appendix, as well as 'other hyper-parameter settings' are being refered to the appendix which AAAI does not add. The authors do not provide a link to their appendix anywhere, so this is not considered part of the documentation. This value could be 1-3 if either this was linked or AAAI added appendices.

### 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 authors specify the data split for training validation and test set. The authors state how many runs are performed and the present the average plus standard deviation. A large explanation is given on the evaluation and metrics.

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

[3]

The method requires an understanding of geometric deep learning, stastical biases w.r.t graphs and general fairness in neural networks. The topics are well introduced by the authors and clear definitions are given, helping the reader on their way. 
