Run Yang¹, Yuling Yang¹, Fan Zhou¹, Qiang Sun^2
1Shanghai University of Finance and Economics, ²University of Toronto

We introduce a novel class of models termed directional diffusion models (DDM), which adopt data-dependent, anisotropic, and directional noises in the forward diffusion process. This code is an implementation of DDM on 12 public graph datasets.

• IMDB-B
• IMDB-M
• COLLAB
• REDDIT-B
• PROTEINS
• MUTAG

• CORA
• Citeseer
• PubMed
• Ogbn-arxiv
• Amazon-Computer
• Amazon-Photo

conda create -n ddm python=3.8
conda activate ddm
cd ddm-nni
pip install -r requirements.txt

cd to EXP path(MUTAG for example)

cd GraphExp
python main_graph.py --yaml_dir ./yamls/MUTAG.yaml

In view of the sensitivity of diffusion method to hyperparameters, it is recommended to use hyperparameter search methods like NNI to achieve better results Trust me ! In this way, you can achieve better results than what is presented in the paper

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