Compositional Fairness Constraints for Graph Embeddings
Authors: Avishek Bose, William Hamilton
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments We investigated the impact of enforcing invariance on graph embeddings using three datasets: Freebase15k-2374, Movie Lens-1M5, and an edge-prediction dataset derived from Reddit. |
| Researcher Affiliation | Collaboration | 1Mc Gill University 2Mila 3Facebook AI Research. |
| Pseudocode | No | The paper describes algorithmic steps in paragraph form but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code to reproduce our results is available at: https://github.com/joeybose/ Flexible-Fairness-Constraints. |
| Open Datasets | Yes | We investigated the impact of enforcing invariance on graph embeddings using three datasets: Freebase15k-2374, Movie Lens-1M5, and an edge-prediction dataset derived from Reddit. 4www.microsoft.com/en-us/download/details.aspx?id=52312 5grouplens.org/datasets/movielens/1m/ |
| Dataset Splits | No | Given this graph, the main task is to train an edge-prediction model on 90% of the user-subreddit edges and then predict missing edges in a held-out test set of the remaining edges. The paper specifies train and test splits, but no explicit validation split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper mentions using multi-layer perceptrons and leaky ReLU activation functions but does not specify any software libraries or dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | The paper states, 'The Appendix contains details on the exact hyperparameters (e.g., number of layers and sizes) used for all the different experiments, as well as details on the training procedures (e.g., number of epochs and data splits),' indicating that these details are not provided in the main text. |