On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections
Authors: Peizhao Li, Yifei Wang, Han Zhao, Pengyu Hong, Hongfu Liu
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical validation demonstrates that our method delivers effective dyadic fairness in terms of various statistics, and at the same time enjoys a favorable fairness-utility tradeoff. |
| Researcher Affiliation | Academia | 1Brandeis University, 2University of Illinois at Urbana-Champaign {peizhaoli,yifeiwang,hongpeng,hongfuliu}@brandeis.edu hanzhao@illinois.edu |
| Pseudocode | Yes | Algorithm 1: Algorithmic routine for Fair Adj |
| Open Source Code | No | The paper does not provide an explicit statement or link to its open-source code. |
| Open Datasets | Yes | We conduct experiments on real-world social networks and citation networks including Oklahoma97, UNC28 (Traud et al., 2011), Facebook#1684, Cora, Citeseer, and Pubmed. |
| Dataset Splits | No | The paper mentions "train/test splits" but does not explicitly detail a separate validation split or its proportion/use. |
| Hardware Specification | Yes | Experiments are conducted on Nvidia Titan RTX graphics card. |
| Software Dependencies | No | The paper mentions various methods (e.g., VGAE, node2vec, Fairwalk) but does not specify software or library version numbers for reproducibility. |
| Experiment Setup | Yes | For all experiments, we set T1 = 50 and the total epochs which contain T1 and T2 equal to 4. Graph neural networks are applied with two hidden layers with size 32 and 16 respectively. ηθ is set to 0.01. For η e A for different datasets, we have: Oklahoma97: 0.1; UNC28: 0.1; Cora: 0.2; Citeseer: 0.5. |