Diffusion Models on Graphs with HydraGNN

This project builds on HydraGNN, leveraging its powerful GNN and ML utilities for training, testing, and model optimization.

Features

• TBD

Quick Start

Clone the repo:

git clone <tbd>
cd <tbd>

Install Dependencies:

Make sure you have the HydraGNN environment set up:

pip install -r requirements.txt

Run Training:

python <tbd>

How It Works

HydraGNN integration: We utilize the operational utilities from HydraGNN, such as model training, testing, and optimization, to simplify workflow. Diffusion Process: Modeled on graph structures to simulate the propagation of information or features across the graph nodes. Perfect for dynamic systems! Model Parallelization: Thanks to HydraGNN, training large models with multi-GPU support is integrated.

️Configuration

All model and training parameters can be easily set via our config.json file:

model:
  type: diffusion_gnn
  layers: 5
  hidden_dim: 128
train:
  epochs: 100
  batch_size: 32
  learning_rate: 0.001

Modules

src/<>.py:

Performance

Our diffusion-enhanced GNNs show promising results in tasks such as:

Contributing

We welcome contributions! If you're interested in extending the diffusion model or improving performance, feel free to submit a pull request or open an issue.