Interpreting Unfairness in Graph Neural Networks via Training Node Attribution
Authors: Yushun Dong, Song Wang, Jing Ma, Ninghao Liu, Jundong Li
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify the validity of PDD and the effectiveness of influence estimation through experiments on real-world datasets. |
| Researcher Affiliation | Academia | 1University of Virginia 2University of Georgia {yd6eb, sw3wv, jm3mr, jundong}@virginia.edu, ninghao.liu@uga.edu |
| Pseudocode | Yes | Algorithm 1: Node Influence on Model Bias Estimation |
| Open Source Code | Yes | Open-source code can be found at https://github.com/yushundong/BIND. |
| Open Datasets | Yes | Four real-world datasets are adopted in our experiments, including Income, Recidivism, Pokec-z, and Pokec-n. Specifically, Income is collected from Adult Data Set (Dua and Graff 2017). Recidivism is collected from (Jordan and Freiburger 2015). Pokec-z and Pokec-n are collected from Pokec, which is a popular social network in Slovakia (Takac and Zabovsky 2012). |
| Dataset Splits | No | The paper frequently mentions using a "test set" for evaluation, but it does not specify explicit training/validation/test splits (e.g., percentages or counts) or a cross-validation strategy. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions using GCN as the backbone model, but it does not specify version numbers for any software dependencies (e.g., specific Python, PyTorch, or library versions). |
| Experiment Setup | Yes | Specifically, we set Γ = λΓSP + (1 λ)ΓEO and estimate the node influence on Γ to consider both statistical parity and equal opportunity. We then set a budget k, and follow the strategy adopted in Section to select and delete a set of training nodes with the largest positive influence summation on Γ under this budget. We set λ = 0.5 to assign statistical parity and equal opportunity the same weight, and perform experiments with k being 1% (denoted as BIND 1%) and 10% (denoted as BIND 10%) of the total number of training nodes. |