CrediGraph

Data analysis for TG/RAG project @ CDL

Getting Started

Prerequisites

The project uses uv to manage and lock project dependencies for a consistent and reproducible environment. If you do not have uv installed on your system, visit this page for installation instructions.

Note: If you have pip, you can invoke:

pip install uv
# or
brew install uv

Installation

# Clone the repo
git@github.com:ekmpa/CrediGraph.git

# Enter the repo directory
cd CrediGraph

# Install core dependencies into an isolated environment
uv sync

# The isolated env is .venv
source .venv/bin/activate

Usage

Running full data processing scripts

cd bash_scripts

./end-to-end.sh /bash_scripts/CC-Crawl/CC-2025.txt

Running GNN Baseline Experiment

Given the size of our datasets we must leverage mini-batching in our GNN experiments. To do this we use PyG's neighbor_loader, which requires additional libraries having undocumented build-time dependencies. As such, users are required to install them in their own venv. seperate from uv sync.

pyg-lib:

uv pip install pyg-lib -f https://data.pyg.org/whl/torch-${TORCH}+${CUDA}.html

PyTorch Sparse:

uv pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.7.0+${CUDA}.html

For information on installations of these additional libraries see pyg-lib and PyTorch Sparse.

To run our baseline static experimentation:

uv run tgrag/experiments/main.py

Alternatively, you can design you own configuration, updating the model paramaters:

uv run tgrag/experiments/main.py --config configs/your_config.yaml

To learn more about making a contribution to CrediGraph see our contribution guide