Skip to content

Latest commit

 

History

History
72 lines (58 loc) · 1.9 KB

README.md

File metadata and controls

72 lines (58 loc) · 1.9 KB

GraphSage

The goal of this project is to reproduce the paper "Inductive Representation Learning on Large Graphs".

Folder structure

  • baselines/ Code for all the baselines.

  • experiments/ Code reproducing all the experiments.

    • fig2a/ Code reproducing the figure 2a of the original paper.
    • fig2b/ Code reproducing the figure 2b of the original paper.
    • fig3/ Code reproducing the figure 3 of the original paper.
    • table1/ Code reproducing the table 1 of the original paper.

  • graphsage/ Code related to graphsage models and extensions.

    • datasets/ Code related to datasets and transformations.
    • models/ Code related to models.
    • layers/ Code related to reusable pytorch modules.
    • samplers/ Code related to samplers.
    • trainers/ Custom abstractions to monitor training.

  • examples/ Direct application of models on datasets.

  • docs/ Auto-Generated & manual code documentation.

  • scripts/ Contains bash scripts, this scripts might just be launchers for python scripts defined in the main package. Useful for running long experiments for example.

  • data/ Auto-generated, contains original or intermediate synthetic data.

  • examples/ All the examples, python scripts or notebooks, illustrating the usage of the package.

  • graphsage/ Python package containing the main code for this project.

  • results/ Auto-generated, For results, e.g. tables (csv files), and plots (images)

Installation

# Create the conda environment
conda env create -f env.yml
# Add the environment to your jupyter kernels 
python -m ipykernel install --user --name graphsage 
# Activate the environment
conda activate graphsage

Tests Documentation

To generate documentation, run

scripts/makedoc.sh

The documentation entrypoint will be generated at docs/_build/html/index.html