Learning Fair Graph Representations via Automated Data Augmentations
Authors: Hongyi Ling, Zhimeng Jiang, Youzhi Luo, Shuiwang Ji, Na Zou
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our Graphair consistently outperforms many baselines on multiple node classification datasets in terms of fairness-accuracy trade-off performance. |
| Researcher Affiliation | Academia | Hongyi Ling, Zhimeng Jiang, Youzhi Luo, Shuiwang Ji , Na Zou Texas A&M University College Station, TX 77843, USA {hongyiling,zhimengj,yzluo,sji,nzou1}@tamu.edu |
| Pseudocode | Yes | We summarize the training algorithm for Graphair and provide the pseudo codes in Algorithm 1. |
| Open Source Code | Yes | Our code is publicly available as part of the DIG package (https://github.com/divelab/DIG). |
| Open Datasets | No | The paper mentions 'NBA is extended from a Kaggle dataset' and 'Pokec-z and Pokec-n are sampled from a larger social network Pokec' but does not provide specific links, DOIs, repository names, or formal citations with authors and year for direct public access to these datasets. |
| Dataset Splits | Yes | we randomly split 10%/10%/80% for training, validating and testing the classifier. ... We randomly split 20%/35%/45% for training, validating and testing the classifier. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions using GCN models and Adam optimizer but does not provide specific version numbers for any software dependencies (e.g., Python, PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | For Graphair, we adopt two-layer GCN models as the adversary model k and augmentation encoder genc, and a three-layer GCN model as the representation encoder f. We use 64 as the hidden dimension in all three models. For the augmentation model, we use an MLP model with 2 layers, the hidden size of 64, and Re LU as the non-linear activation function for MLPA and MLPX. The hyperparameter β is set to 1, and the hyperparameters α, γ and λ are determined with a grid search among {0.1, 1, 10}. ... We train the models for 500 epochs using Adam optimizer with 1 × 10−4 learning rate and 1 × 10−5 weight decay. |