Random Walk Diffusion for Efficient Large-Scale Graph Generation

Official implementation of the ARROW-Diff graph generation method proposed in the paper "Random Walk Diffusion for Efficient Large-Scale Graph Generation". ARROW-Diff is a novel approach for large-scale graph generation based on random walk diffusion. It generates graphs by integrating two components into an iterative procedure: (1) An order agnostic autoregressive diffusion model on the level of random walks that learns the generative process of random walks of an input graph, and (2) a GNN component that learns to filter out unlikely edges from the generated graph. Due to the random walk-based diffusion, ARROW-Diff efficiently scales to large graphs, significantly reducing the generation time.

Overview of ARROW-Diff.

Getting Started

Run conda install -r requirements.txt to install the required packages.

Usage

The code comprises two main components:

1. Training of the diffusion model and the GNN
2. Graph generation (sampling)

ARROW-Diff Training

Please change the name of the dataset and other parameters in configs/config.yaml and run the training script using the command python main_arrow_diff_training.py.

ARROW-Diff Graph Generation

Please set the graph_generation parameters in configs/config.yaml and run the graph generation script using the command python main_arrow_diff_graph_generation.py.