Directional Diffusion Models

NeurIPS 2023

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.

Graph classification datasets

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

Node classification datasets

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

Framework

Usage

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

Performance

Directional noise v.s. white noise

Graph classification(F1-score)

	IMDB-B	IMDB-M	COLLAB	REDDIT-B	PROTEINS	MUTAG
GIN[1]	75.1±5.1	52.3±2.8	80.2±1.9	92.4±2.5	76.2±2.8	89.4±5.6
DiffPool[2]	72.6±3.9	-	78.9±2.3	92.1±2.6	75.1±2.3	85.0±10.3
Infograph[3]	73.03±0.87	49.69±0.53	70.65±1.13	82.50±1.42	74.44±0.31	89.01±1.13
GraphCL[4]	71.14±0.44	48.58±0.67	71.36±1.15	89.53±0.84	74.39±0.45	86.80±1.34
JOAO[5]	70.21±3.08	49.20±0.77	69.50±0.36	85.29±1.35	74.55±0.41	87.35±1.02
GCC[6]	72	49.4	78.9	89.8	-	-
MVGRL[7]	74.20±0.70	51.20±0.50	-	84.50±0.60	-	89.70±1.10
GraphMAE[8]	75.52±0.66	51.63±0.52	80.32±0.46	88.01±0.19	75.30±0.39	88.19±1.26
DDM	76.40±0.22	52.53±0.31	81.72±0.31	89.15±1.3	75.74±0.50	91.51±1.45

Node classification(F1-score)

Dataset	Cora	Citeseer	PubMed	Ogbn-arxiv	Computer	Photo
GAT	83.0 ± 0.7	72.5 ± 0.7	79.0 ± 0.3	72.10 ± 0.13	86.93 ± 0.29	92.56 ± 0.35
DGI[9]	82.3 ± 0.6	71.8 ± 0.7	76.8 ± 0.6	70.34 ± 0.16	83.95 ± 0.47	91.61 ± 0.22
MVGRL[7]	83.5 ± 0.4	73.3 ± 0.5	80.1 ± 0.7	-	87.52 ± 0.11	91.74 ± 0.07
BGRL[10]	82.7 ± 0.6	71.1 ± 0.8	79.6 ± 0.5	71.64 ± 0.12	89.68 ± 0.31	92.87 ± 0.27
InfoGCL[11]	83.5 ± 0.3	73.5 ± 0.4	79.1 ± 0.2	-	-	-
CCA-SSG[12]	84.0 ± 0.4	73.1 ± 0.3	81.0 ± 0.4	71.24 ± 0.20	88.74 ± 0.28	93.14 ± 0.14
GPT-GNN[13]	80.1 ± 1.0	68.4 ± 1.6	76.3 ± 0.8	-	-	-
GraphMAE[8]	84.2 ± 0.4	73.4 ± 0.4	81.1 ± 0.4	71.75 ± 0.17	88.63 ± 0.17	93.63 ± 0.22
DDM	83.4±0.2	74.3±0.3	81.7±0.8	71.29±0.18	90.56±0.21	95.09±0.18

References

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