Reproducibility study of FairAC

An implementation of "Fair Attribute Completion on Graph with Missing Attributes" paper.

To run experiments:

Install dependencies using conda

conda env create -f environment.yml

Code structure

The code is organised as follows:

• dataset/ contains the dataset files
• src/models/ contains PyTorch models for FairAC/FairGNN
• experiments/ contains notebooks to run the experiments

Run the experiments

The experiments can be run using the notebooks in the experiments/ folder. The notebooks are self-explanatory and can be run in order to reproduce the results.

Evaluation of the provided model weights

We provide the pre-trained weights from our experiments in experiments/logs. These weights can be evaluated using the experiments/run_fair_ac.ipynb notebook. The notebook is self-explanatory and can be run to evaluate the model weights.

Deepwalk embeddings

Several deepwalk embeddings are provided already. If more are needed they can be created by running:

python src/create_deepwalk_emb.py

Results

To convert the LaTeX table to Markdown format, the table will be simplified to fit Markdown's capabilities, as Markdown does not support complex table structures (like merged cells or mathematical symbols) directly. Here's a simplified version:

Dataset	Method	M	Acc ↑	AUC ↑	ΔSP ↓	ΔEO ↓	ΔSP+ΔEO ↓	Consistency ↑
NBA	GCN	✔	66.98 ± 1.18	76.15 ± 1.40	0.14 ± 0.13	0.57 ± 0.06	0.71 ± 0.18	2.64 ± 0.00
	ALFR	✖	64.3±1.3	71.5±0.3	2.3±0.9	3.2±1.5	5.5±2.4	-
	ALFR-e	✖	66.0±0.4	72.9±1.0	4.7±1.8	4.7±1.7	9.4±3.4	-
	Debias	✖	63.1±1.1	71.3±0.7	2.5±1.5	3.1±1.9	5.6±3.4	-
	Debias-e	✖	65.6±2.4	72.9±1.2	5.3±0.9	3.1±1.3	8.4±2.2	-
	FCGE	✖	66.0±1.5	73.6±1.5	2.9±1.0	3.0±1.2	5.9±2.2	-
	FairGNN	✔	68.39 ± 3.12	74.29 ± 1.19	2.81 ± 4.01	3.00 ± 4.07	5.81 ± 8.08	2.64 ± 0.00
	FairAC (Ours)	✔	66.51 ± 1.09	75.69 ± 1.31	0.09 ± 0.08	0.10 ± 0.00	0.19 ± 0.08	2.64 ± 0.00
Pokec-z	GCN	✔	65.10 ± 0.24	68.42 ± 0.12	1.72 ± 1.17	1.37 ± 0.51	3.08 ± 1.68	41.35 ± 0.01
	ALFR	✖	65.4±0.4	71.3±0.3	2.8±0.5	1.1±0.4	3.9±0.9	-
	ALFR-e	✖	68.0±0.6	74.0±0.7	5.8±0.4	2.8±0.8	8.6±1.2	-
	Debias	✖	65.2±0.7	71.4±0.6	1.9±0.6	1.9±0.4	3.8±1.0	-
	Debias-e	✖	67.5±0.7	74.2±0.7	4.7±1.0	3.0±1.4	7.7±2.4	-
	FCGE	✖	65.9±0.2	71.0±0.2	3.1±0.5	1.7±0.6	4.8±1.1	-
	FairGNN	✔	68.16 ± 0.59	75.67 ± 0.52	1.56 ± 0.45	3.17 ± 1.07	4.73 ± 1.47	41.35 ± 0.01
	FairAC (Ours)	✔	65.33 ± 0.30	71.20 ± 1.74	0.55 ± 0.10	0.13 ± 0.15	0.68 ± 0.09	41.33 ± 0.00
Pokec-n	GCN	✔	67.88 ± 1.46	72.86 ± 1.44	3.22 ± 1.29	5.93 ± 2.76	9.15 ± 4.05	45.93 ± 0.00
	ALFR	✖	63.1±0.6	67.7±0.5	3.05±0.5	3.9±0.6	3.95±1.1	-
	ALFR-e	✖	66.2±0.4	71.9±1.0	4.1±1.8	4.6±1.7	8.7±3.5	-
	Debias	✖	62.6±1.1	67.9±0.7	2.4±1.5	2.6±1.9	5.0±3.4	-
	Debias-e	✖	65.6±2.4	71.7±1.2	3.6±0.9	4.4±1.3	8.0±2.2	-
	FCGE	✖	64.8±1.5	69.5±1.5	4.1±1.0	5.5±1.2	9.6±2.2	-
	FairGNN	✔	67.06 ± 0.37	71.58 ± 2.58	0.55 ± 0.50	0.30 ± 0.20	0.85 ± 0.31	45.93 ± 0.00
	FairAC (Ours)	✔	67.00 ± 1.93	72.57 ± 1.68	0.11 ± 0.06	0.47 ± 0.81	0.58 ± 0.76	45.94 ± 0.02

Table 1: Comparison of FairAC with FairGNN on the nba, pokec-z and pokec-n dataset.

• The methods are applied on the GCN classifier, and the values for the baselines are taken from the original paper.
• The values for FairAC, FairGNN and GCN are taken from our experiments, that can be recreated using the notebooks under experiments/.
• The values consist of the mean and standard deviation of the metric over 3 runs on the seeds 40, 41 and 42.
• The best results are denoted in bold.