https://github.com/salesforce/gaea
Data and code for Salesforce Research paper, GAEA: Graph Augmentation for Equitable Access via Reinforcement Learning - https://arxiv.org/abs/2012.03900 . The paper provides methods for constraint graph augmentation and optimal facility placement problems
https://github.com/salesforce/gaea
ai constraint-optimization deep-reinforcement-learning equity fairness-ai graph-algorithms graph-machine-learning graph-ml ml reinforcement-learning resource-management rl social-network social-network-analysis
Last synced: 8 months ago
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Data and code for Salesforce Research paper, GAEA: Graph Augmentation for Equitable Access via Reinforcement Learning - https://arxiv.org/abs/2012.03900 . The paper provides methods for constraint graph augmentation and optimal facility placement problems
- Host: GitHub
- URL: https://github.com/salesforce/gaea
- Owner: salesforce
- License: bsd-3-clause
- Created: 2020-12-02T19:53:47.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2023-07-06T22:25:21.000Z (over 2 years ago)
- Last Synced: 2024-12-27T13:02:57.550Z (9 months ago)
- Topics: ai, constraint-optimization, deep-reinforcement-learning, equity, fairness-ai, graph-algorithms, graph-machine-learning, graph-ml, ml, reinforcement-learning, resource-management, rl, social-network, social-network-analysis
- Language: HTML
- Homepage:
- Size: 8.63 MB
- Stars: 9
- Watchers: 6
- Forks: 2
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
- Code of conduct: CODE_OF_CONDUCT.md
- Codeowners: CODEOWNERS
- Security: SECURITY.md
Awesome Lists containing this project
README
# GAEA: Graph Augmentation for Equitable Access via Reinforcement Learning
This repository contains the data and code for [Salesforce Research](https://einstein.ai) paper: [GAEA: Graph Augmentation for Equitable Access via Reinforcement Learning
](https://arxiv.org/abs/2012.03900)
## Citation
If you use this code, data or our results in your research, please cite as appropriate:
```
@inproceedings{ramachandran2021gaea,
title={GAEA: Graph Augmentation for Equitable Access via Reinforcement Learning},
author={Ramachandran, Govardana Sachithanandam and Brugere, Ivan and Varshney, Lav R and Xiong, Caiming},
booktitle={Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society},
pages={884--894},
year={2021}
}
```
## Prerequisites
### Software
Install dependencies by running
```
pip install -r requirements.txt
```
The code was tested on
```
cuda 11.2
tensorflow-gpu==2.6.0
keras==2.6.0
```
### Hardware
We ran on a Quadro GV100 with 32GB RAM.
### Dataset
1. Dataset merging public census, school, and transportation datasets for the city of Chicago is provided under data/{demographics | network | schools}
2. For Facebook100 dataset download the data as described in http://sociograph.blogspot.com/2011/03/facebook100-data-and-parser-for-it.html and place the unziped data under data/facebook100
## Experiments
Edit repository path and the output path for the project in paths_inc.py .
The run_experiments.py generates all results for:
1. Original graph
2. Baseline method
3. Proposed method
On each of the outputted graphs, we run monte carlo weighted walk simulations and estimate the distribution of expected rewards of walkers. On this distribution, we evaluate our main two criteria:
1. Expected Utility
2. Gini Index of Expected Utility
### Graph editing on Chicago school network
```python run_experiments.py --exp edit --graph chicago```
### Graph editing on Facebook100 schools
```python run_experiments.py --exp edit --graph fb --school Caltech36```
Other school network we tried are: Mich67 and Reed98
### Graph editing on synthetic network
```python run_experiments.py --exp edit --graph synthetic```
### Facility Placement
```python run_experiments.py --exp facility_placement --graph chicago```